KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY

[.rcmitv'KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY

(Approved by AICTE & Govt of T.S and Affiliated to INTUH)3-5-1026, Narayanaguda, Hyderabad-29. Phi 040-2326f407

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KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY(Approved by AICTE & Govt of T.S and Affrliated to JNTUH)

3-5-1026, Narayanaguda. Hyderabad-29. Ph: 040-23261407

Vision of the Institution:

To be the fountain head of latest technologies,

producing highly skilled, globally competent engineers.

Mission of the Institution:. To provide a leaming environment that helps studenb to enhance problem solving skills,

be successful in their professional lives and to prepare students to be llfelong learners through

multi model platforms and educating them about their professional, and ethical responsibilities.

. To establish Industry institute Interaction to make students ready for the industry.

. To provide exposure to students to the latest tools and technologies in the area of hardware and

software,

. To promote research based pqects/activities in the emerging areas of technology convergence.

. To encoura8e and enable students to not merely seek jobs from the industry but also to create

new enterprises

o To induce in the students a spirit of nationalism which will enable the student to develop and

u nderstan d

lndia's problems and to encourage them to come up with effective solutions for the sameTo support the faculty in their endeavors to accelerate their learning curve in order

to continue to deliver excellent service to students

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l{mitDepartment Of Information Technolory

Vision & Mission of Department

Vision of the Department:

Producing quality graduates trained in the latest software technologies andrelated tools and striving to make India a world leader in software products andservices.

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Mission of the Department: To create a faculty pool which has a deepunderstanding and passion for algorithmic thought process.

To impart skills beyond universify prescribed to transform students into awell-rounded IT professional.To inculcate an ability in students to pursue Information technologyeducation throughout their lifetime by use of multimodal leamingplatform including e-leaming, blended learning, remote testing andskilling.Exposure to different domains, paradigms and exposure to the financialand commercial underpinning of the modem business environmentthrough the entrepreneur development cell.To encourage collaboration with various organizations ofrepute forresearch, consultancy and industrial interactions.To create socially conscious and emotionally mature individuals withawareness on lndia's challenges, opportunities, their role andresponsibility as engineers towards achieving the goal ofjob and wealthcreation.

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KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY(Approved by AICTE & Go!.t of T.S and Afiiliated to JNTUH)

3-5- I026, Narayanaguda, Hyderabad-29. Ph: 040-2326t.407

Mission of the Department:

KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY(Approved by AICTE & Govt of T.S and Affiliated to JNTUH)

3-5- 1026, Narayanaguda, Hyderabad-29. Phl 040-23261407

PROGRAM OUTCOMES (POs)

1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering

fundamentals, and an engineering specialization to the solution of complex engineering

problems.

2. Problem analysis: Identifu, formulate, review research literature, and analyze complex

engineering problems reaching substantiated conciusions using first principles of

matlematics, natural sciences, and engineering sciences.

3. Design/development of solutions: Desigrr solutions for complex engineering problems

and design system components or processes that meet the specified needs with

appropriate consideration for the public health and safety, and the cultural, societal, and

environmental considerations.

4. Conduct investigations of complex problems: Use research-based knowledge and

research methods including design of experiments, analysis and interpretation of data, and

synthesis of the information to provide valid conclusions.

5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and

modem engineering and IT tools including prediction and modelling to complex

engineering activities with an understanding of the limitations.

6. The engineer and society: Apply reasoning informed by the contextual knowledge to

assesssocietal, health, safety, legal and cultural issues and the consequent responsibilities

relevant to the professional engineering practice.

7. Environment and sustainability: Understand the impact of the professional engineering

solutions in societal and environmental contexts, arrd demonstrate the knowledge of, and

need for sustainable development.

8. Ethics: Apply ethical principles and commit to professional ethics ard responsibilities

and norms ofthe engineering practice.

Department Of Information Technology

9. Individual and team work: Function effectively as an individual, and as a member or

leader in diverse teams, and in multidisciplinary settings.

10. Communication: Communicate effectively on complex engineering activities with the

engineering community and with society at large, such as, being able to comprehend and

write effective reports and design documentation, make effective presentations, and give

and receive clear instructions.

ll.Project management and finance: Demonstrate knowledge and understanding of the

engineering and management principles and apply these to one's oram work, as a member

and leader in a team, to manage projects and in multidisciplinary environments.

12. Life-long learning: Recognize tle need for, and have the preparation and ability to

engage in independent and lifeJong leaming in the broadest context oftechnological change.

KmitKESHAV MEMORIAL INSTITUTE OF TECHNOLOGY

(Approved by AICTE & Covt of T.S and Affiliated to JNTUH)3-5- I026, Narayanaguda, Hyderabad-29. Ph: 040-23261407

Department Of Information Technolory

PROGRAM SPECIFIC OUTCOMES (PSOs)

PSOI: An ability to analyze the common business functions to design and

develop appropriate Information Technology solutions for social upliftments.

PSO2: Shall have expertise on the evolving technologies like Mobile Apps,

CRM, ERP, Big Data, etc.

g KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY(Approved by AICTE & Go\4 of T.S and Alnliated to JNTUH)

3-5- 1026, Narayanaguda, Hyderabad-29. Ph: 040-23261407

Department Of Information Technologr

PROGRAM EDUCATIONAL OBJECTMS (PEOs)

PEO1: Graduates will have successful careers in computer related engineeringfields or will be able to successfully pursue advanced higher education degrees.

PEO2: Graduates will try and provide solutions to challenging problems intheir profession by applying computer engineering principles.

PEO3: Graduates will engage in life-long leaming and professional

development by rapidly adapting changing work environment.

PEO4: Graduates will communicate effectively, work collaboratively and

exhibit high levels ofprofessionalism and ethical responsibility.

Rl3Phoner Otr: +91 40-231561 l5

F!x:+91-.40 23158665

JAWAHARLAL NEHRU TECHNOLOGICAL UNTVERSITY HYDERABAD(Estabtished by Andhra Ptadesh Act No.30 of 2008)

Kukatpally, Hyderabad - 50O 085, Andhra Pradesh (India)

I YEAR

IIYEAR ISEMESTER

II YEAR II SEMESTER

III YEAR I SEMESTER

B, TECH. INFORMATION TECHNOLOGY MPUTER SCIENCE AND TECHNOLOGY

Code Subject L "lP tDEnqlish 2Mathematics - | 3 1 6Mathematical Methods 3

Enqineerinq PhYsics 3 6Enqineerinq Chemistry 3 6

Comouter Proorammina 3

Enqineerinq Drawinq 2Comguter Proqrammino Lab 3

Enqineerinq Physics / Enqineerinq Chembtry Lab 3 4Enqlish Lanouaqe Communication Skills Lab 3 4lT Workshoo / Enqineerinq WorkshoD 3 4

Codo S ubject L rIPD cProbability and Statistics 4Mathematical Foundations of ComputerScience

4 4

Data Structures 4 4Digital Logic Oesign and ComputerOroanization

4 4

Electronic Devices and Circuits 4 4Basic Eleclrjcal Engineering 4 4Electrical and Electronics Lab 3 2

Dala Slrudures Lab 3 2

Tota I 24 6

Codo Subioct L rIPDPrinciples of Proorammnn Lanquaqes 4 4Database Management Systems 4 4Java ProgrammirE 4 4Environmental Studies 4 4Data Communication 4 4Design and Analysis ofAlqorithms 4 4Java Prooramminq Lab 3 2Database Manaaement Svslems Lab 3 2Total 6 28

Codo Subject L TIPIDAutomata and Compiler Design 4Linux Programmino 4 4Software Engineerinq 4 4Operating Systems 4 4Computer Networks 4 4Managerial Economics and FinanCia t nnarysis 4 4Operaling Systems Lab 3 2Computer Networks Lab (Through Linux) 3 2Tota I 24 28

cmmsi 'TECHNOLOGI"'E Mail: &qi!Sb1@ssil,s!Ee

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III YEAR II SEMESTER

Code Subjecl L TIPIOWeb Technolog ies 4OPEN ELECTMEHLman Values and Professbnal Ethicslntelhclual Propedy RightsDisaster Manaqement

4 4

Obiecl Oriented Analysb ard Desiqn 4 4Data Warehousing and Data Mining 4 4Software Testino Methodoloqies 4 4Cloud Compdinq 4 4Data MiniE and Web Technobsies Lab 3 2Advanced Enqlish Communication Skills Lab 3 2Total 24 6 28

IV YEAR I SEMESTER

IV YEAR II SEMESTER

Code Subject L T/P/OManaqement Scienc€ 4 4ELECTIVE IIIWeb ServicasE - Cornmerceiriddleware TechnobgiesAd hoc and Sensor Netwo*s

4 4

ELECTTVE IVMultimedia & Rich lnternet ApplicationsArtif ichl lntellig€nceStorage Area Neh,vorksMachine Learnim

4 4

lndustry Oriented [4ini Project 2Seminar 6 2Proiect Work 15 10

'C-ompielensive Viva 2

Total- 12 21

Note: All End E)@minatbns Ctheory and Practical) are ofthree hours duratbn.T Tutorial L-Theory P - PracticauDrawing C Credits

Code Subjest L TIPIDlnfo.mation Secu.itv 4Desiqn Patterns 4 4Mobile ADplicatbn Develogment 4 4lnf ormation Retrieval Systems 4 4ELECTIVE - I

Wieless Networks and Mobile Computinglmage ProcessirE and Pattern RecognitionSoft CornputingSemanticWeb and Social NetworksOperations Resaarch

4 4

ELECTIVE - IISoftware Projed ManagementComputer GraphicsHuman Computer lnteraclionScrhting LanguagesComputer Fo.ensics

4

Case Toob and Software Testing Lab 3 2

Mobile Applications Development Lab 3 2Total 6

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JAWAHARLAL NEHRU TECIINOLOGICAL UNIVERSITY HYDERABADil:sxtblisht,l bl',4ndltu I'rld.tslt ltt N{,. J0 of 2l,l)3)

Kukatp ll). tl!{lcrlba(l i0(, 0SJ, -l'clln8rxa (l[din)

Ilr. IJ.ltJ. BHANDARIl,h,D( TXGI').

Prof'essor ol Elect. & Cotntnn. Engg., &Director,Academie & Planning

Lr.No:A1/ Academic Calendar/8. "Iech & B. Pharm./2016 Dated: 10.06.2016

To

The Principals of Constituent Colleges.

The Principals of Affiliated Engineering/Pharmacy colleges of JNTUH

5ir,

Sub:- JNTUH, Hyderabad - Academic & Planning -Approval of Academic Calendar for ll,lll and lV years of B. Tech and B, Pharmacy I & ll Semester for the academic year

2016-17 * Communicated.

The Academic Calendar for Il, lll and lV years of B. Tech and B. Pharmacy l& llSemester (Regular) for the academic year 2016-17 is approved. The details are as follows:

lSemester:

Description Period DurationCommencement of Class Work r.3.06.2 016

First Spell of lnstructions 13.06.2016 to 06.08.2016 (8w)First Mid ExaminationsTimings:10.00 am to 12.00 Noon(Forenoon Session)02.00 prn to 4.00 pm

(Afternoon Session)

Second Spell instructions

08.08.2016 to 13.08.2016 (1 w)

(7 w)

Dussehra Holidays 05.10.2016 to 12.10.2016 (1 w)

Supplementary ExaminatioIs 13.10. 2016 to 26.10.2016

2.7 .rO.70L6 b A3,L1.2016 (1 w)Second Spell continuation

16.08.2016 to 04.10,2016

Secorrd Mid Examinatiorrs

Tinrings: 10.00 anr to L2.00 Noon (Forenoon

Sessron)02.0o pm to 4.00 l]) Afternoon Scssion

lrre arations and Practical [xaminations .t1. 1L. 201 6 to 17.).1.2076End sernester Ixaminations 18. 11.2016 to 01..1.2.201G (2w

ll Sern(-'ster

oescription I\:riod D u rnt i.)IComrnr-'ncemsnt of class work 02.12.2016

Iirst Spell of lnstructions

Dussehra holidays from 05,10.2016 to 12.10.20L6 may clrange subiect to thedirections from the Government of Telangana

Yours faithfully

04.L 1.2016 to 10.1,1.2016

<):-DIRECTOR

Copv to:The Director of EvaluationThe Controller of Examinations.P.A to VC, Rector and Registrar

(1w)

(1wi

02. 12. 20116.to 27.01.201 7 (Bw)First Mid Exarninations

Timings: 10.00 am to L2.00 Noon(Forenoon Session)02.00 pm to 4.00 pm(Aftcr oon Session)

28,0L.2O17 to 04.02.20L7 (1w)

05.02. 2017 to 18.O2.2077 (2w)Supl)lementary Examinations

Secor rd Spellof lnstructions 19.02.2017 to 14.04.20L7 {8w}Second Mid ExaminationsTimirlgs:10.00 am to 12.00 Noon (Forenoon

Session)

02.00 pm to 4.00 pm (Afternoon Session)

15.04.2017 to 21.O4.2017 (1w)

Preparation and Practical Examinations 22.O 4.2017 to 28.O 4.2017 (1 w)

29.04.2077 to 12.05.2017 (2 w)End semester examinationsSummer Vacation 13.05.2017 to 77.06.2077 (4w)

Conlrrencement of class work for the nextacadernic year 20X6-17

13.06.2017

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KESHAV MEMORIAL INSTITUTE OF TECHNOLOGYDEPARTMENT OF INFORMATION TECHNOLOGY

COURSE - PLAN - One copy to be submitted to the HOD one week before commencement of the semester

Rivision

SignatuDate

Coordinator Sign he FacultyDate

Subiect

Code

Name of theS u bject

Class/5em Name of the

Faculty /Oesignation

Number of

Students

Total Proposed Periods

per semester/year

lnformation

Retrieval

Systems

IV IT Dr.Ramakanth

Mohanthy/ Professor.

Lectu res Tutorial

t 57 11

Week

Number

UnitNumber

Lecture

Nu mber

Oate ofCompletion

Topic

L3/6/201.6 BB1 lntroduction

2 14/6/2076 BBAbout IRS

BB3

4 BBTutorial:Retrieval Strategies: Vector

Space Model

Precision and Recall 1.s161201.6

16/6/2A76

5 Vector Space Model 1.5/6l2016 BB

1

6 Vector Space Model 1.816120L6 BB

7 Probabilistic retrieval strategies: Simple

term weights

20/6/2076

8 Probabilistic retrieval strategies: Simple

term weights

2L/6120L6 BB

9 22/6/2016 BB

10 Tutoria l:Example on the concept

Contd

231612016 BB

11, Example 2s/6/?016 BB

2

t2 Non binary independence model 251612076 BB

13 Non binary independence model 2716/2016 BB

14 BB

1,

BB3

tb Tutoria|:Retrieval Utilitres:Bneflng

l, zt/a/zorc29/6/2076

3016/201.6

Language Models

BB

L7 Relevance Feed Back 2/7 /2016 BB

18 Relevance Feed Back 2/7 /2u.6 BB

19 Clustering s/7 12016 BB

20 Clustering 617 /20L6 BB

4

21, Tutorial:N-G ra ms for error correction 7 /7 120L6 BB

21, BB

22 BB

23 BB

24

Retression Analasys

Tutoria l:Thesuari

Regression Analasys

Thes u a ri

L1/71201.6

74/7 /2016

1.2/7 /2OL6

t3/71201.6

8B

3

a1 ,-45 t6/7 /?01.6 9.8

*This column has to be filled-up after completion of the lecture/tutorial/practical in rhe copy kept with the faculty members

A70533I

I

RemarksI

I

I

BB

II

I

I rs Language Models.i

I

I

!

Semi-Structured search using relationa I

schema

L4/9/2016

Tutorial:briefing 15/9/20L6

'::i:'w*'ordinator Facultyte

26 Retrieval Utilities: Semantic Networks 78/7 /20L6 BB

BB27 Sema ntic Networks 1.9/712016

28 Parsing 2017 /201.6 BB

BB29 Pa rsin g 2317 /201.6

30 Co ntd 2s17 /201.6 BB

31 Cross Language lnfo Retrieval 26/7 /20L6 BB

27 /7 /201.6 BB30 lnlroduction

Tutorial:Work jng with CLIR 28/7 /201.6 BB

BB33 t\,4et hod s 3017 /207634 30/7 /20L6 BB

3

35 contd. 16/812016 BB

36 Efficiency: lnverted index introd uction BB

37 Tutorial:Semantic Search 78/8/20L6 BB

BB

8

39

40 BB

Building inverted index

Methods

20/8120L6

22/8/2016

41, 23/8/2016 BB

42 Query Processing 2418/201.6

43 BBTutorial: DisambiBUation 2s/8/20L6

9

2718/201.6 BB

45 Du plicate document detection 2s/81201.6

46 3A/8/20L6 BB10

47 Contd 31.18/201.6 BB4

Revision 3/s/2u.6 BB

lntegrating Structured Data and Text AHistorical Progression

3/e1201.6 BB

50 Contd 6/e/201.6 8B

51 lnformation Retrieval as a Relationalapplication

8B1,L

BBTutorial: lnformation Retrieval as a Relationalapplication

8/e1201.6

Semi-Structu red search using relational

sche ma

12/9/20t6 BB

54 BBSemi-Structu red search using relational

schema

13/9/2016

55 BB

12

56 BB

*This column has to be filled-up after completion of the lecture/tutorial/pract cal in the copy kept with the faculty members

KESHAV MEMORIAI INSTITUTE OF TECHNOIOGYDEPARTMENT OF INFORMATION TECHNOTOGY


BB

BB

BB

6

7 32

Crossing the Language Barrier.

1.7 /812016

20/8/2016

Exa m p les

Query Processing

44 S,gnature Files

Du plicate document detection

48

49

7 /9/20L6

52

5 53

REFERENCES:

KESHAV MEMORIAL INSTITUTE OF TECHNOLOGYDEPARTMENT OF INFORMATION TECHNOTOGY


TEX'I' BOOKS :

L. lnformation Retrieval - Algorithms and Heuristics, David A. Grossman, Ophir Frieder, 2nd

Edition, 2012, Springer, (Distributed by Universities Press)

1. lnformation Storage and Retrieval Systems: Theory and lmplementation By (owalski, Gerald,MarkT Maybury,Springer.

2. Soumen Chakrabarti, Mining the web:Discovering Knowledge from hyper text data,Morgan-Kaufmann publishers,2002.

3. Christopher D.Manning, Prabhakar Raghavan, Hinrich Schutz, An lnatroduction to informationRetrieval,Cambridge U nivercity Press, Cambridge, Engla nd,2009.

the Coordinator si tu cue

Date

51 Distributed information retrieval 17 /9/2016 BB

58 Distributed information retrieval

Distributed information retrieval

17 /9/2016 BB

13

59 Revis io n L9/9/20L6 BB

60 Theoretical model of distributed

retrieval

20l9/201.6 BB

61 Theoretical model of distributed

retrieval

21/912016 B8

5

62 Tutoflal:Web Search 22/9120L6 BB

63 Web Search 24/9/20L6 BB

1,4

64 Web Search 26/s/201.6 BB

65 Theoretical model of distributedretrieva I

27le/201.6 BB

66 Contd 28/9/2016 BB

67 Rivision 29/9/2016 BB

68 Previous Papers BB

column has to be filled-up after completion ofthe lecture/tutorial/practical in the copy kept with the faculty members

ItY

I

I

1/r0/2016

KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY

DEPARTMENT OF IT

SUB: IRS

YEARJSEM: IV/I CLASS: IT

S.NO Topics beyond the syllabus Text book/web reference

1 Semantic Search TI

2 Disabiguation TI

3 N-grams TI

4 Some clustering techniques TI

TOPICS BEYOND THE SYLLABUS

Semantic search seeks to improve search accuracy by understanding thesearcher's intent and the contextual meaning of terms as they appear in the searchabledataspace, whether on the Web or within a closed system, to generate more relevantresults. Semantic search systems consider various points including conte)d of search,location, intent, variation of words, synonyms, generalized and specializedqueries, concept matching and natural language queries to provide relevant searchresults.r'r Major web search engines like Google and Bing incorporate some elements ofsemantic search. ln vertical search, Linkedln publishes their semantic search approachto job search by recognizing and standardizing entities in both queries and documents,e.9., companies, titles and skills, then constructing various entity-awared features basedon the entities.('?1

Guha et al. distinguish two major forms of search: navigational and research.r3r lnnavigational search, the user is using the search engine as a navigation tool to navigateto a particular intended document. Semantic search is not applicable to navigationalsearches. ln research search, the user provides the search engine with a phrase whichis intended to denote an object about which the user is trying to gather/researchinformation. There is no particular document which the user knows about and is trying toget to. Rather, the user is trying to locate a number of documents which together willprovide the desired information. Semantic search lends itself well with this approachthat is closely related with exploratory search.

Rather than using ranking algorithms such as Google's PageRank to predict relevancy,semantic search uses semantics, or the science of meaning in language, to producehighly relevant search results. ln most cases, the goal is to deliver the informationqueried by a user rather than have a user sort through a list of loosely related keywordresults. However, Google itself has subsequently also announced its own SemanticSearch project.ror

Author Seth Grimes lists "11 approaches that join semantics to search", and Hildebrandet al. provide an overview that lists semantic search systems and identifies other usesof semantics in the search process.r5r

Other authors primarily regard semantic search as a set of techniques for retrievingknowledge from richly structured data sources like ontologies and XML as found onthe Semantic Web.rurSuch technologies enable the formal articulation of domainknowledge at a high level of expressiveness and could enable the user to specify theirintent in more detail at query time.rl

ln order to understand what a user is searching for, word sense disambiguation must occur. When aterm is ambiguous, meaning it can have several meanings (for example, if one considersthe lemma "bark", which can be understood as "the sound of a dog," "the skin of a tree," or "a three-masted sailing ship"), the disambiguation process is started, thanks to which the most probablemeaning is chosen from all those possible.

Such processes make use of other information present in a semantic analysis system and takes intoaccount the meanings of other words present in the sentence and in the rest of the text. Thedetermination of every meaning, in substance, influences the disambiguation of the others, until a

D is am-bi guati onleo it1

situation of maximum plausibility and coherence is reached for the sentence. All the fundamentalinformation for the disambiguation process, that is, all the knowledge used by the system, isrepresented in the form ofa semantic network, organized on a conceptual basis.

ln a structure of this type, every lexical concept coincides therefore with a semantic network nodeand is linked to others by specific semantic relationships in a hierarchical and hereditary structure. lnthis way, each concept is enriched with the characteristics and meaning ofthe nearby nodes.

Every node of the network (called Synset) groups a set of synonyms which represent the samelexical concept (called Synsets) and can contain:

. single lemmata ('seat', 'vacation'; 'work', 'quick'; 'quickly', 'more', etc,)

. compounds('non-stop','abal-jour','policeman')

. collocations ('credit card', 'university degree', treasury stock', 'go forward', etc.)

The semantic relationships (links), which identify the semantic relationships between the synsets,are the order principals for the organization of the semantic network concepts.

N-Grams

ln the fields of computational linguistics and probability, an n-gram is a contiguoussequence of n items from a given sequence of text or speech. The items canbe phonemes, syllables, letters, words or base pairs according to the application. The n-grams typically are collected from a text or speech corpus. When the items arewords, ,?-grams may also be called shingles.t'l

An n-gram of size 1 is referred to as a "unigram"; size 2 is a "bigram" (or, lesscommonly, a "digram"); size 3 is a "trigram". Larger sizes are sometimes refened to bythe value of n in modern language, e.9., "four-gram", "five-gram", and so on.

An ,-gram model is a type of probabilistic lanquaqe model for predicting the next item in such asequence in the form of a (ri - 1)-order Markov model.B n-gram models are now widely usedin probabilitv, communication theorv, computational linquistics (for instance, statistical naturallanouaqe orocessino) , computational bioloqv (for instance, biological sequence analvsis), and datacompression. Two benefits of n-gram models (and algorithms that use them) are simplicity andscalability - with larger n, a model can store more context with a well-understood sDace-timetradeoff, enabling small experiments to scale up efficiently.

n-gram models

An n-gram model models sequencesJ notably natural languages, using the statistical

This idea can be traced to an experimenl by Claude Shannon's work in information theorv. Shannonposed the question: given a sequence of letters (for example, the sequence "for ex''), what isthe likelihood of the next letter? From training data, one can derive a probabilitv distribution for the

next letter given a history of size :a=0.4, b=0.00001, c=0,,...; where the probabilities of allpossible "next-letters" sum to 1.0...

More concisely, an n-gram model predicts based on , ln probability terms, this isWhen used for lanquaqe modelino, independence assumptions are made so that each word

,properties of n-grams,

depends only on the last n - 1 words. This Markov model is used as an approximation of the trueunderlying language. This assumption is important because it massively simplifies the problem ofestimating the language model from data. ln addition, because of the open nature of language, it iscommon to group words unknown to the language model together.

Note that in a simple n-gram language model, the probability of a word, conditioned on somenumber of previous words (one word in a bigram model, two words in a trigram model, etc.) can bedescribed as following a cateoorical distribution (often imprecisely called a "!!u!!!sDiarcIsti!u'tjon")

ln practice, the probability distributions are smoothed by assigning non-zero probabilities to unseenwords or n-grams; see smoothino technioues.

Disambiguation is the process of resolving conflicts that arise when a potential articletitle is ambiguous, most often because it refers to more than one subject covered byWikipedia, either as the main topic of an a(icle, or as a subtopic covered by an article inaddition to the article's main topic. For example, the word "Mercury" can refer to achemical element, a planet, a Roman god, and many other things.

There are three important aspects to disambiguation:

Naming articles in such a way that each has a unique title. For example, three of thearticles dealing with topics ordinarily called "Mercury" are titled Mercury(element), Mercury (planet) and Mercury (mythology).Making the links for ambiguous terms point to the correct article title. For example,an editor of an astronomy article may have created a link to Mercury, and thisshould be corrected to point to Mercury (planet).Ensuring that a reader who searches for a topic using a particular term can get tothe information on that topic quickly and easily, whichever of the possible topics itmight be. For example, the page Mercury is a disambiguation page-a non-articlepage which lists various meanings of "Mercury" and which links to the articles thatcover them. (As discussed below, however, ambiguous terms do not always requirea disambiguation page.)

What is Clustering?Clustering can be considered the most important unsupervised learning problem; so,as every other problem of this kind, it deals with frndirrg a structure in a collection of

unlabeled data.A loose definition of clustering could be'the process of organizing objects into

groups whose members are similar in some way".A cluster is therefore a collection ofobjects which are "similar" between them and are

"dissimilar" to the objects belonging to other clusters.We can show this with a simple graphical example:

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In this case we easily identifu the 4 clusters into which the data can be divided; thesimilaritv criterion is distance: two or more obj ects belong to the same cluster if theyare "close" according to a given distance (in this case geometrical distance). This is

called distance-based clustering.Another kind ofclustering is conceptual clustering: two or more objects belong to thesame cluster if this one defines a concept common to all that objects. In other words,

objects me grouped according to their fit to descriptive concepts, not according tosimple similarity measures.

The Goals of ClusteingSo, the goal of clustering is to determine the intrinsic grouping in a set of unlabeled

data. But how to decide what constitutes a good clustering? It can be shown that thereis no absolute "best" criterion which would be independent of the final aim of the

clustering. Consequently, it is the user which must supply this criterion, in such a waythat the result of the clustering will suit their needs.

For instance, we could be interested in hnding representatives for homogeneousgroups (data reduction), in finding "natural clusters" and describe their unknown

properties ("natural" data types), in finding useful and suitable groupings ("useful"data classes) or in finding unusual data objects (outlier detection).

Posslb/e ApplicationsClustering algorithms can be applied in many fields, for instance:

Marketing: finding groups of customers with similar behavior given a largedatabase of customer data containing their properties and past buying records;Biology: classification of plants and animals given their features;

.i.r:.:';.i':i

. Libraries: book ordering;

. Insurance: identifring groups of motor insurance policy holders with a highaverage claim cost; identifuing frauds;

. City-plannirzg: identiffing gtoups of houses according to their house type,value and geographical location;

. Earthquake studies: clustering observed earthquake epicenters to identi!dangerous zones;

. WWW: document classification; clustering weblog data to discover groups ofsimilar access patterns.

RequirementsThe main requirements that a clustering algorithm should satisfl are:

. scalability;

. dealing with different types of atkibutes;

. discovering clusters with arbitrary shape;

. minimal requirements for domain knowledge to determine input parameters;

. ability to deal with noise and outliers;

. insensitivity to order of input records;

. high dimensionality;

. interpretability and usability.

ProblemsThere are a number of problems with clustering. Among them:

current clustering techniques do not address all the requirements adequately(and concurrently);dealing with large number of dimensions and large number of data items can beproblematic because of time complexity;the effectiveness of the method depends on the definition of "distance" (fordistance-based clustering);if an obvious distance measure doesn't exist we must "define" it, which is notalways easy, especially in multi-dimensional spaces;the result of the clustering algorithm (that in many cases can be arbitrary itself)can be interpreted in different ways.

Clustering Algorithms

ClassificationClustering algorithms may be classified as listed below:

. Exclusive Clustering

. OverlappingClustering

. HierarchicalClustering

. Probabilistic Clustering

In the first case data are grouped in an exclusive way, so that ifa certain datumbelongs to a definite cluster then it could not be included in another cluster. A simple

example of that is shown in the figure below, where the separation of points isachieved by a straight line on a bi-dimensional plane.

On the contrary the second type, the overlapping clustering, uses fuzzy sets to clusterdata, so that each point may belong to two or more clusters with different degrees of

membership. In this case, data will be associated to an appropriate membership value.

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Instead, a hierarchical clustering algorithm is based on the union between the twonearest clusters. The beginning condition is realized by setting every datum as a

cluster. After a few iterations it reaches the final clusters wanted.Finally, the last kind of clustering use a completely probabilistic approach.

In this tutorial we propose four of the most used clustering algorithms:

. K-means

. Fuz4 C-means

. Hierarchicalclustering

. Mixture of Gaussians

Each ofthese algorithms belongs to one of the clustering types listed above. Sothat, K-means is ar exclusive clustering algorithm, Fuzry C-means is xr overlappingclustering algorithm, Hierarchical clustering is obvious and lastly Mixture of

60 o(lu

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Gaussian is a probabilistic clustering algorithm. We will discuss about each clusteringmethod in the following paragraphs.

Distance MeasureAn important component of a clustering algorithm is the distance measure betweendata points. If the components of the data instance vectors are all in the same physicalunits then it is possible that the simple Euclidean distance metric is sufficient tosuccessfully group similar data instances. However, even in this case the Euclideandistance can sometimes be misleading. Figure shown below illustrates this with an

example of the width and height measurements of an object. Despite bothmeasurements being taken in the same physical units, an informed decision has to bemade as to the relative scaling. As the figure shows, different scalings can lead todifferent clusterings.

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Minkowski MetricFor higher dimensional data, a popular measure is the Minkowski metric,

I

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Notice however that this is not only a graphic issue: the problem arises from themathematical formula used to combine the distances between the single componentsof the data feature vectors into a unique distance measure that can be used forclustering purposes: different formulas leads to different clusterings.Again, domain knowledge must be used to guide the formulation of a suitable distancemeasure for each particular application.

where d is the dimensionality of the data. The Euclidean distance is a special casewhere y2, while Manhattar metric has p:1. However, there are no generaltheoretical guidelines for selecting a measure for any given application.

It is often the case that the components of the data feature vectors are not immediatelycomparable. It can be that the components are not continuous variables, like length,but nominal categories, such as the days ofthe week. In these cases again, domainknowledge must be used to formulate an appropriate measure.

KESHAV MEMORIAT INSTITUTE OF TECHNOTOGYDEPARTMENT OF INFORMATION TECHNOLOGY


Signature o, the Coordinator signature of the FacultyDate Date*This column has to be filled-up after completion ofthe lecture/tutorial/practical in the copy kept with the faculty members.

Subject

Code

Name of theSubiect

Class/Sem Name of theFaculty /

Designation

Number ofStudents

Total Proposed Periods per

semester/year

A70533

IV IT

Dr. Ramakanta

Mohanthy/ ProfessorLectures Tutorial

60

Unlt

Number

Topic Web References

1

lntroduction

About IRS httDs://n lo.sta nford. ed u/lR-book/informatio n- retrieva l. htmlPrecision and Recall

Retrieval Strategies:

Vector Space Model

htto://oeoole.cs.eeorsetown.edu /-nazli/classes/ir-Slides/Boolea n-Ve ctorSpace- 11. pdf

Probabilistic retrieval

strategies: Simple term

weights

http://home page s. inf.ed.ac. uk/vlavren k/doc/pmir-1x2. pdf

Non binary independencemodel

httls://nlp.sta nford,edu/l R-book/html/htmledition/the-rna n el- tml

Non binary independencemodel

http://people.ene. unimelb.edu.au/tcohn/comp90042/l9.pdf

Language Models httos://nl o.stanford.edu/l R- /html/htmled ition/laneuage-

models-for-information-retrieval-1.html

2 Retrieval Utilities: RelevanceFeed Back

htt ps://n lp.sta nford. ed u/lR-book/pdf/09expa nd. odf

Relevance Feed Back https://www. cs. ucv. a c. cvlco u rses/EP 1660/lectu res/lectu re8-

rel. pdf

Clustering

N-Grams

Regression Analysis https://www. ia re.ac.inlsites/d efa ult/fi les/lectu re notes/lR5%

2OLECTURE%2ONoTEs.pdf

Thesauri httDs b.sta nfo rd.ed u class/cs276lhandouts/lecture20-

distributed-representations.pdf

Retrieval Utilities:

Sema ntic Networks

https://classes. soe. ucsc.ed u/ism293/S prins09/m ateria l/Lectu

Semantic Networks

lnformation

Retrieval

Systems

https://n lo. sta nford. ed u/lR-book/informatio n- retrieva l, htm I

https://en.wikipedia.orslwiki/Precision and recall

http://people.cs.seorgetown.edu/-nazli/classes/ir-

SIides/Boolean-VectorSpace-13.pdf

Vector Space Model

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1. 1.71

0.6923&rep=reo1&tvoe=pdf

http://theses. gl a.a c.uk/ 340 /

re%2O2.odf

https:/,/en.wikipedia.orslwiki/Semantic network

KESHAV MEMORIAT INST]TUTE OF TECHNOLOGY

DEPARTMENT OF INIORMATION TECHNOLOGY

couRsE - pLAN - one copy to be submitted to the HoD one week before commencement of the semester

TEXT BOOKS :

1. lnformation Retrieval - Algorithms and Heuristics, David A. Grossman, Ophir Frieder, 2nd Edition,

2012, Springer, (Distributed by Universities Press)

REFERENCES :

1. Modern lnformation Retrieval Systems, Yates, Pearson Education 2. lnformation Storage and Retrieval

Systems, Gerald J Kowalski, Mark T Maybury, Springer, 2000

Signature ofthe Coordinator SiBnature ofthe FacultyDate Date*This column has to be filled-up after completion of the lecture/tutorial/practical in the copy kept with the faculty members.

Parsing

Cross Language lnfo

Retrieval

htto://disi.unitn.itl-bernardi/Courses/DL/clir.odf

Crossing the lnformationBarrier.

4

Efficiency: lnverted index

introduction

lnverted index https://nlp.sta nford.edu/ lR-book/html/htmledition/a-f irst-

take-at-buildins-an-inverted-index-1.html

Query Processing https://www.slideshare.net/silambu111/i nformation-

retrieval-s

Signature Files https://en.wikipedia,orglwiki/Signature file

Duplicate document

detectionlntegrating Structured Dataand Text: A HistoricalProgression

lnformation Retrieval as aRelational application

https://nlp.stanford.edu/l R-book/pdf/10xml.pdf

Semi-Structured search

using relational schema

Distributed information

retrieval

Distributed information

retrieval

https://www.cs.helsinki.fi /u/hahonen/irm07/lectures/irm0712.odf

Theoretical model ofdlstributed retrieval

https://nlp.sta nford.edu/lR-book/pdf/12lmodel.pdf

Web Search

Web Search lttBi?www.dsi.unive.itl-dm/Slides/5 info-retrieval.pdfWeb Search

htto://www.academia.edu/252376lParsing in lnformation E

xtraction and Retrieval

htt o://www. crectiru pati.com/sites/d efa u lt/files/notes

https://e n.wikiped ia. orslwiki/lnve rted index

http://0roiects. ict. usc.ed u/n ld/ir-

class/sites/prolects. ict. usc.ed u. n ld. ir-cla ss/files/slid es/04. pdf

5

https://en.wikipedia.orslwiki/lnformation extraction

htt ps://e n.wikiped ia.orelwiki/Se mi-structu red data

http://d s. cs. ut. ee/cou rses/ds-sem ina r-m ateria lslPa ra lle l-a n d-

Distributed lnfo rmation- Retrieva. pdf

htto://www.dais.unive.itl-dm/New Slides/8 info-

retrieval.pdf

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l-Qllkvlew ls a leadtng Buslness Dlscovery Platform, lt ts very powerful tnvlsually analyslng the relauonshlps between data. It does in-memorydata processlng stores data ln the report ltself whlch it creates. lt canread data from numerous sources lncludlng flles ond relaflonaldatabases. It ls used by buslnesses to get deeper lnslght by doingadvanced analytlcs on the data thcy have, It even does data lntegrarlonby comblnhg data from varlous sources lnto one QllkMew analyslsdocument. QllkVlew is a leadlng +uslness Intelllgence and AnalytlcsPlatform ln Gartner Maolc Ouadranlj

AudienceThfs tutorlal ls deslgned for all those readers who want to create, read,wrlte, and modlfy Buslness lntelllgence Reports ustng Qltkvlew. Inaddltlon, lt wlll also be quite useful for those readers who would like robecome a Data Analyst or Data Sclentlst.

. Prerequisites1

'r Before Froceedlng wlth thls tutorlal, you should have a baslc..understandlng of Computer Programmlng termlnologles. A baslcunderstandlng of any of the programmlng languages wlll help you inunderstandlng the Qllkview programmlng concepts. Famlllarity with SQLr/Yill help you learn it very fast.

QlikVlew ls a leading Buslness Dlscovery Platform. lt ls unlgue in manyways as compared to the traditional BI platforms. As a data analysis toolit always malntains the relationship between the data and thisrelationship can be seen visually uslng colors. Even lt also shows whlch

data are not related! It provldes both dlrect and lndirect searches byusing indivldual searches on the llst boxes'

Qlikview's core and patented technology has the feature of in memorydata processlng which gives superfast result to the users. It calculatesaggregations on the fly and compresses data to 10o/o of original size.

Neither users nor developers .of QlikVlew appllcations manage ther-elationship between data' it ls managed automatlcallyJ-.J

(Features of QlikView'[titviu* has patented technology which enables it to have manyFeatures that are useful in creating advanced reports from multiple datasources quickly. Below is a list of features which makes QlikView veryunique.

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@Datr A.soclstlon 13 mslnt lnGd automrdcally - ellkvlew automatica[y recognlzes therelatlonslilp b€tween each plece of data that ls present In a datageL Users need notprcconflgurc the relauonshlp bGtween diffet3nt data en fl€s.

Datr lt hald ln memory tot mulflplE u3er3, for a super-L3t urcr axp6rlenc6 - Thestructure, data and col6jlauoo5 of I report are all held ln the rmmory (RAM) of theseryer.

Ag0regEtlon! rra calculEtad on tha fly ar needed - A5 the data is held lfl memory,calculatlons are done on the fty. No need of storlng pre-calculaled aggregate values.

Oata 13 compre3sed to ,,Oo,t of lts odglnal slrs - etikvlew heavily uses data dlc onaryand orlly essentlal blts of data ln memory requlred ffi any anatysls. Hence it compressesthe orlginal data to a very small slze.

vbual relattonshtp udng coloE - The relauonshtp between datr ls not shown bf anowor llnes but by colors, Selecung s pleca ol data gtves speciflc colo6 to the related dataand another color to unrelated data

. Dlrect and Indlrect s5rrc.hr3 - Instead of glvtng the dlrcct yalue a user ts tooklng for,they can lnput some related dlta 3nd qet the etact rcsult because of the dataassodauofi. Of course th€y can also search for a value dtretflyl"l

Download QlikView -J

The Free Personal Edition of QllkView can be downloaded from QlikVlewPersonal Edition. You need to reglster with your detalls to be able todownload..

After downloading, the installation ls a very straight forward process inwhich you need to accept the license agreement and provide the targetfolder for installation. The below screen shots describe the entire setupprocess.

Architectural OveruiewQlikview's architecture consists of a front end to visuallze the processed

'='data and a back end to prgvide the security and publicitlon mechanl$nfor .QlikView user documents. ,The below diagram deplcts how Qlikviewint6rnally works. The various parts of the architecture are discussed.indetail below the Picture.

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Front EndThe Front end in Qlikvfew is a browser based access point for viewing theQlikview documents. It contains the QlikView Server which is malnlyused by the Business users lo access the already created BI reportsthrough a internet or intranet URL. Business users explore and interactwith data using thls front end and derlve concluslons about the data.They also collaborate with other users on a given set of reports and bysharing inslghts, exploring data together, in real tlme or ofF-line. Theseuser documents are in the format .gvw whlch can also be stored in thewindows OS as a standalone document. The Qlikview server in the Frontend manages the client server communication between the user and

Back EndThe QlikView backend consists of QllkView desktop and QlikViewpublisher.

The Qlikvaew desktop is a wizard driven wlndows environment whichhas the features to load and transform data from source. It's drag anddrop feature is used to create the GUI layout of the reports that becomesvisible in the f rontend. The f ile types which are created by Qlikview

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@desktop are stored wlth an extenslon of ,qvw. These are the flles that arepassed on to the ellkvlew server ln the front end which serves the usersw.lth these flres. Also ,qvw fires can be modtfled to store the data-inryfiles whrch are known as .qvd fires. They are brnary fires whrch containonly the data and not the GUI components.

The Qlikview publlsher rs used as dlstrlbuuon sei.rrce to drstribute the.qvw documents among varlous ellkVlew servers and users. It handlesthe authorizatlon and access privlleges, it also does the dlrect loadrng ofdata from data sources by using the connecflon strlngs deflned ln the.qvw files.

Gr*"L,r+hilc no

h tr!3l. But t{r sholrld considcr Ihat ve'vc aclully had more dalt lh wc ran rcasonably us. fo, aw. Just on thc morkcting fror|t,

liom ofunde yirg datsit isn't urcomDon to see reporls ovcrrlowing with daa ad bcnchma*s

drawn &om mil poinB coyrrlnB u.i3ting charmcls likc dlsplay, cmail, wcbsite, scarch,srid lhopper/loyalty - ![ld new daa slrcams such a! socisl srd mobilc eoBqgtrncr , rqview!, commcnti,ratings, localioo check'ins and rnore

ln cor$rrn ro thk sburdant data, irsights arc rglativcly nra. lnsights h?rr arc &fincd as lrtiorBbtc, dabdlivcnfindings thal creatc h$incss valuc, Thcy arc cntint dilrcrsn bcast5 *om rav datr. DcliveriDg thcm rcquiresdifercrt pcople, tcchmlos/, and skius - spcclfically includlng d.cp domain knowlcdgc. Ard thcy'rc hard lobuild

Ewn lvith greal drto and tools, insights can be cxceptioially to!g! lo comc by. Conridcr 0ra improvingNaflix's recomme ndqtion+ngirc accuracy by abofi l0 provcd so chellcngiry thlt only two tcams - oflenEofrhousands fiom over 180 c.ountriG conpetirg for rhcsl millior prizc - ivcrc able ro hir th. eool. Or that,

dcspirc significant wut lo improve online dispta) ad laBeliDg, the avcrdgc clicklhrough rate (tnd, b)imolicilion. rehvlnce) srill rcmains so lo\v thal disDlav ods on sverocc ralliyeonllClldlsLfgr-fv-e!}!-000y.iq\5. That is, the va5l majority ofpeople who sce the ad don'l think it's interesdng or rthvanl cnouS"h lo.lickon

N'hen rh.y arc gencirt€d, rhough, insigrrls deriycd ftom the lman u5c ofdita aE hugcE poivlrful- Bmnds and

compuics rhar are able lo dawlop big insighls - fron aDy le!'el of data - $ ill bc lYin ers.

tlcre's n four-sl.p markering darr-centcred proctss lhal doern't sloP al thc dala. brll foeu3.s iElcad ott

generaring insights rclcvant to lpccilic scgnenE o. affmity 8,oups:

, . l. Colelt GSod dstn js_the founda_llir fq the proccss-.Dals,can bc collecled from sottrc6 a5 vati!{ as blols.

.. ..f."$, fT.l"l.luY:* 9-ne1q-en1 lgn,rns' rcvic\a's, ad engiErment. and wcbsitc cli*ittEam'. ' ' t '

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2. Conncct, Somc data will simply bc us€firi in rhe iggngale (for eramplc. to look at brold rends), Othct dala,

|o\ycver. is morc actionable if ir's conlccted lo specific segments or ct(n individuals. lmponanlly, the linkirg

of 5ocial/digital d0la lo indivirluals rvitl require obtailing conruncr conrcnl ond comPlyi[g $hh lo(al

rcgulations.

3. Mrnep.. Civcn the speed and volume ofsocial inleraction onlin., simply msnagi!8 bi8, dlra requires special

ie.fnioul. aleorithms and srorage soturions. And, while somc data c!l1 bc stoEd, olhct typca of dao ar.

u..",r"d in ,"fl-,ir" or only for a timilcd time via ABI!'

J. l\nxl|7c xn Discovcr. rhis pan olrhe proutss *orks bcsl whcn il's ;t btoadl)' collaborative one. LlsinS

srarisr,cs, rcponing and visualiz-1tion tools, n'nrkclers, product managers, and rlau scienlists work logelher to

come up *,itr tlre icy insighls lhal $'ill gcneral. valuc broadly' for specific 5'gmcnE ofcustomcE a')d.

ultimarrly p€rsonalizcd insights for irrdividucl customcrs'

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Conlldcr lhesc llli8lfls - drarvn from dcrailed studic5 and data lnalylis- lhar arc bclng u5./ by us and olh.rsto dclivcr voluo todlyt

Frllnd3' lnlcfest! mth lds more rclw.rt. Boscd on $e svalultion of3oclal-8]raph data ond clicks,comp0nl€s ruch as SlActoss hsvc found thst showing 8dr bilci on friends'simllar intcrcsts canlubllonli6lly lEiS! id gliglqharurgrdg-EEs,

sonr[mct lt't okry lf pcoplc hllc )'oor Tv !iow. A telcvlsiotr rclwo coflrmilsiorrd ogilyy to look at thcrcl[tiomhlp bcN{lsn socltrl medla buzz lnd r6tlrgs. An snrlysis ofthoBands of socirt mcdii dau poins andNlclscn ratingr afi*s t0 nctwort and cablc stroua ltartOci vrys to hctp pndict ratln8! changcs aod fmd thrspctlfic plol llnss ondehlrlctcrs lhat couH bG emph8l2rd ln markcrlng rb ddvc higlcr vicweBhh. Oft in3ightwtr th[t it's crllically imponsnt to loak rt darr dlllcEntly b) ihoiv ard g.nrc. As m cxamplc, for lomc rcalitytnd ncrvly-lnunched cablc siorvs, both lovc llrd tBte - ar lorg a3 thcrc was lots of i! * drovr audiatcc ralitgt.

lirclol nrcdln rvorks belt ir combln&llo[. Mcasuring 0rc adual business irnpaq oIsoci.l mcdia and cross-trcdi0 intcractions (beyordjust impr.$ions) is in rhc cady stagcs and cruld bavc pcrhaF the mo3t profoundimpacts ofall on mtking mffkcting bcncr and morE eflicicnt, For examplc, by cxploring panrl-lascd d.la onbrnnd crco{nlcts by socially-engdgad cll3tomcys in th! rlsiawant indutry, Ogllvy rndgtafiIlg(tr forttd lhatsochl mcdi! was very cflbctlve h drivinS rlrcnuc iom fhk lcgrn€nr. Ho\Ncycr, t rit Gff.ct was strongcsl *hcnrcclal mcdia wlre coDblncd with orhcr channcb snri ,s t'adidona.l PR rnd ot(.ofjlomr mcdia, Exposurc lolhesc combinrtion! dtovc l.5x to 2x increascs in rlE lltalihood ofrcvcnuc 8ains,

Each of thosc insights works because it i: rclionablc rtd gcnqratcs value. E ch ooc providcs a conoltc road

msp for meking markctirg mora offcc1lw Dnd cflleicnt And aPPlying tach insiBht crlatc! valuc thar both

brdnds and consumsrs csn apprcci!

,-

s1212017

Introduction to InformationRetrieval

Jian-Yun Nie

University of Montreal

Canada

The problem of IR. Goal = frnd documents relevartto an information

need from a large document set rnro.

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Outline

r What is the IR problem?

, How to organize an IR system? (Or themain processes in IR)

.Indexing

. Retrleval

. System evaluation

. Some current research topics

1

s1212017

IR problemapplications: in libraries (1950s)

l5Al|r &201-12227€Allthor: Salt n, Gar6.dTltL: Automatk @xt p.esrrE: th€ transfomation,

ana\ds, and renie!"| of i6fofm.tlo.r by compute.Edltor A(kiison-WesleyD.t : 1989

external attributes and internal attribute (content)Search by external attributes = Search in DBIR: search by content

Possible approaches

1, String matching (linear search indocuments)- Slow- Dimcu[ to improve

2. lndexing (*)- Fast

- Flexible to further improvement

Indexing-based IR

Document Query

indexing indexing(Query analysis)

Representation Representation(keywords) Query (keywords)

evaluauon

Main problems in IRDocument and query indexing. How to best represent their contents?

. Query evaluauon (or retrieval proce6s). To what extent does a document correspond

to a query?

. System evaluation. How good is a system?. Are the retrieved documents rclevant?

(precision). Are all the relevant documents retneved?

(recall)

2

s1212017

Document indexingGoal = Find the important meaninqs and create anintemal representauonFadols to consider:. Acflra€y to r€pres€nt oreanlngs (semanti6). E\t'auslvefl€ss (co/er all the contents). Fadllty fo( comput€r to m.nlpulatE

what is the best representauon of contenb?. orar. stnng (6ar tlEatu): not prEds€ eflolrsh. $lod: good cqrerage, not prrdse. Phrase: @r @r€rage, more F€dse. Conce*: p6r coverage, t)redse

cG4.-r..rr.'c,(R...ll) SEln, woii FnE. Coic.Da (PEddo.)

*l* feyworO selection and weighting

-. How to select imryftantkewords?. Simple method: using mlddle-frequency words

tf*idf weighting schema

S = term frequency. ft€qlerEy d a terfiVksywod ln a cloofiEnt

Ihe hlgh€r t'e f, dre hDher t'€ lmpo.bnc! (wdgtt) for Sle doc.

. df = document frequency. m, of documents aofltahhg tfl€ tefln. dEtnbdoi ofthe term

. idf = inverse document frequency. $e liF/enness ot terrlt ds6hruon h $e @r9!s. $e spedflcity of term to a d6,,me.t

Th€ mor€ the tEfm ls dlstlbuted 6€nr, the less lt ts spedfc to a

weigt'{t D) = t(t o) * id(t)

Some commo n tfxidf schemes

. (l D)=freq(tD) ld(t) = log(N/n)

. t(t, D)=loslfreq(lD)] n = *docs containing t

. t(l D)=la[freq(lD)]+1 N = #docs ln corpus

. t(t, D)=freq(t d)/I1ax[(t.d)]

weight(t,D) = tf(t,D) * ld(O

3

. Normalization: Cosine normalization, /max, ...

s1212017

r Reason:. Different word foms may bear similar meanlng

(e.9. search, searching): create a "standard"reprcsentation for them

r Stemming:. Removing some endings of word

Sto rds / istnction words do not bear useful information for IR

of, in, about, with, I, although, ...Stoplist: contain stopwords, not to be used as index

. Some adverbs and adjectives

. So,ne fiEquent words (e.9. document)

. Th_e removal of stopwords usually improves IRereclveness

. A few "standard" stoplists are commonly used,

Pofter algorithm.l (Porter, l"l.F., 1980, An algorithm for suffix stripping,

d** Pmgran, t4(3) i130-r37)-E-----:-----:--

.' r step rr prurdE ano past paroooes. ssEs -> s5. ev)INC-> rotding -> rEtor

step 2: adi>n, n->v, n->adj,.... (m>0) OUSNESS -> OUS calldsEss -> ellous. (m>0) AnOMAt -> AIE relatiorlal.> relate

Step3:. (m>0) ICq-IE -> IC hdrcate -> Brpric

Step 4:. (m>l)AL -> r*ival-> revtr, (m>1)ANCE-> allowane -> allow

Step 5:

. (m>1) E.> pmbat .> probat

r (m > 1 and rd and rL) -> singre rettlt

4

Document LengthNormalization. Sometimes, additional normalizations e.g.

length:

ptwted(t . D) =weight(t.D)

v,. :t"!" *, ,tEed weryht\t,D)

.1 ^.-_t Stemmrno'w__ - .

s1212077

Lemmatization. transform b standard turm accoding to syntadc

category.E9. vlro + ,l, i Eb

. l.l€ed POg bgglrp, tlo.e aGrr.te than stemmhg, bn rE€ds nrcre r€rairrces

crucial to choose stemming/lemmatization rulesnoise v.s. rE@gnltiofl rate

compromlse between preclsion and recall

lighvno stemming <---------------+ s€\/€y€ stemmlrp-recall +predslon +rccll-predslon

Retrieval

. The problems underlying retrieval. Retrieval model

. How is a dooment repres€nted with thes€lected keywords?

. How are document and query reDresentauonscompared to calojate a score?

. Implementation

Cases

. 1-word query:

The documents to be rebieved are those ulatlnclude the word

. Retsieve the inverted llst for tie word

. sort In decreaslng order of the weight of the word

. Multi-word query?

Cornbining several lists

How to interpret the welght?(IR model)

Each document is represented by a set of weighted

e.g.01J {(comtr4 0.2), (6rdrned, 0.3), ...)D2 J {(conpoL 0.1), (n€t',voi( 0.r, ...}

. Inverd flle:comprn J {(D1,0.2), (02,0.r),...}

I rerd fle i5 used drr|rrg retrl66l br higlE rfildcrq

Result of indexing

keywods (teEr6);o! + {(t!, wr, (t2,w2, .-}

5

r lvlatching score model. Document D = a set of weighted keywords. Query Q = a set of non-weighted keywords. R(D, Q) = :i w(t, , D)

where qis in Q.

sl2120t7

Boolean modelDoorment = l-ogical conjunction of keyrordsQuery = Boolean exprcssion of keywordsR(D,Q)=D-}Q

e.g-

a = (tr ^ t, v (t3

^ -t4)D +Q, stus R(0, Q) = 1.

Problems:. R is €ither 1 or 0 (unofdered set of dooments). many d.a1llrl€nb or few doaJments. End-users cannot manipulate Eoolean operators conectly

E.g, d@ulI]enls a')ul kangat@sand koalas

Extensions to Boolean model(for document ordering)

= {..., (q, w,), ...}: weighred keywords. Interpretation:

. D is a member of class trto degree wr.

. In terms of fuzy setsr h(D) = wl

A possible Evaluation:R(D, q) - |l!(D);R(D, Qr^ Qr) = min(R(D, Q1), R(0, QJ;R(D, Qr v Qr) = ma(R(D, QJ, R(D, Qr);R(D,-Q!)=1-R(D,Q1).

Vector space model

ector space = all the keywords encountered<t! t2, t3' ...' tn>

. Document

D= < dr, dy ?4 -.., dn)ai = weight of q in D

. Querye = < b, b2, b3,..., bn>

bi= weight of q in Q. R(D,Q) = Sim(D,Q)

6

*f IR models

Matrix representationt u.e tr tz t: ,., Qr <_ r*.,*to.D1

D2

D3

D

a b3

drr

dzt?:r

3t:dz:

i::

dtzdzz

6zz

orn

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o61 i62 Et63 .'. 36n

Implementation (space)

Mauix is very sparse: a few 100s terms fora dqcument, and a few terms for a query,while the term space is larse (-100k)

Stored as:D1 --r ((t1, a1), (t2,a2), ...)

tl -+ t(D1,a1),...)

sl2120t7

' The implementation of VSM with dot product:. Nafoe hplementauon: O(m*n). lrnplementation uslng lnverted file:

6tven a query = {(t1,br.), (t2,b2)}:1, llnd UE sets of rclated documenb thrcugh hv€rtcd file for

t1 .td t22, cahrhte ti€ s6.e of tE doq,n€rts to e6dr vreighted tcrm

(t1,b1) + ((Dr,al +br),...)

3. comune th€ s€E ad $m th€ wEighB (t). o(lQl*n)

Some formulas for Sim

Dot product

Cosine

Dice

laccard

Si^lD,Q) =Z@,'b,)

Zto,'b,)s,,,(o,o)-F;_;iYi?2L@,.b,)

sinlD,Q)= Zq, +Lb:

ul.o//o

t2

t(,,'r,)

7

s,,'(o.p)=raIi.;l::(.J6)

*L Implementation (time)i|f---

sl2120t7

Other similaritles

Sin(D,Q)=,@,,b,) I a, b,

iS. it6'"la' ''la'

o'.Ib,'

,* .EF ano .Eul to normalize theweights after indexiilq

Dot product

(Similar operations do not apply to Dice andJaccard)

Probabilistic model. Given D, estimate P(RID) and P(NRID)

. P(RlD)=P(DlRrP(RyP(o)* P(DIR)

D = {tr=x, tr=xr, ...}

. P(Dtx)- fIP(,, =I,tr)

(P(D), P(R) constant)

,={i

= fI P(r, =rlirP(r, =otnI'*'=ne:A-p)t')p(Dt^")= fJp(,, - r I x8f p(,, .o|,vRI'rt=f]a10-{,)" r'

Prob. model (cont'd)

For document rankinq

oaar ot = tn!!l)!!L = rn- PID NR)

fIa"o-aI"''c:0 q )'"''

-5-,,nnp,(l a,)*y1ool:Z?' 'q,tt- p,t a -t-q,

.;,1on4(l-q')

Prob. model (cont'd)

. How to estimate pi and qr? Doc.

Do... A set of /vrelevant and

inelevant samplesi

r n -r,"4"N-4

& N.& N

Inel.do.. Samdes

B

sl2120L7

Prob. model (cont'd)sdd19't=S,.6,s?.1:-9)

? ' - q,(t- P,)

s,- i(N-4 -,, +t)- +\ | R; t'r', t)

. Smoothing (RobeGoi-50rrck-Jon€s to.h!ra)

oaz, nr = s., 1t!14:4:14-i-9ll = s.a' tR_., -ost/n, t,,ost fi'r When no sample is available:

o=0 s,q=('\+0.r/(N+0.stsn /tr

. l'4ay be implemented as VSM

(Classic) Presentation of results

. Query eyaluation result is a list of documents,sorH by their similariv to the query.

. E.g'

docl 0.67

doc2 0.65

do€ 0.54

System evaluationEfflciency: time, space

. Effectiveness;. Hota is a systern capable of retrievino relevant

doclrments?. Is a system better than another one?

. Metrics often used (together):. Preclslon = retsieved relevant docs / retsieved docs. Recall = retsieved relevant docs / relevant do€s

relevant retrieved

9

BM25

s"o,a n ot\ *th'*tvf !!z!E!- * *-to,M -dt' ' ft K+rf k,+qt '''a l+dl

K=k,(t - b\+b d )' ddl dl

. k1, k2, k3, d: parameters

. qtf: query term frequency

. dl: document length

. avdl: average document lenqth

I

sl2l20t7

-Pr€cision change wrt. Recall (nol a fiied point)

-Syslems cannot compare al ooe Precisio Recall point

-Avemgeprecisior(on ll pointsof rerall:0.0,0.1,..., 1.0) 17

. E.g. Rank: 14 rel. doc.

2 rel. doc.3d rel. doc.

lll I I I I )MAP = l-l- + - + -1+ -l- + -\l) 1 I 5 t0 2 4 8"

1

5

10

4

8

Docl Y

Doc2

Doc3 Y

Doc4 Y

An illustration of P/Rcalculation

I

02 04 06 08 l0Assume: 5 relevantdocs

Some other measures

. Noise = retrieved lrrelevant docs / rebieved docs

. Silence = non-retrieved relevant docs / relevant docs. Ncise = 1 - Precisron; gleace = 1- Re(all

. Fallout = retrieved irrel. docs/ irrel. docs, Single value measures:

. F-measure = z P * R / (P + R)

. Ave69e precisrm = average at 11 polnts of rc.allr Precision at n document (often used for Web IR). Expected s€arcf lenq$ (no- irrelevant do.uMts to read

before obbining n relevant do..)

10

ILE-IRAil

I oocs I

t--t---l

*LJFeneral form of precision/recall

;f une (Mean Average Precision)EE--------:--------' u;p='LY-L t .l

n?t&ti-*e r,

. il = rank of the j-h relevant document for Ql

. l&l = #rel. doc. for Q,

. n = # test queries

Test corpus

Compare different IR systems on the sametest corpus

. A test mrpus contains:. A set of documents. A set of querjes. Relevance ,udgment fur every doclment{uery pair

(desired anslvers br each query)

. The results of a system is compared with thedesired ansv'rers.

An evaluation example(SMART)

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,;f. fne TREC experiments{i--i unce per year. A set of documents and queries are distributed

to the partjcipanE (the standard answers areunknown) (April)

. Participants vrork (very hard) to construct, flne-tune their systems, and submit the ansv,/ers(1000/query) at the deadllne (July)

. NIST people manually evaluate the ansrwersand provide conect answers (and classificationof IR systems) (luly - August)

. TREC conference (November)

TREC evaluation methodology. Kno},/n document collection (> l00K) and query set

(s0). Submission of 1000 documents for each query by

eacfi participant. l4erqe 100 frrst documents of each partlcipant ->

global pool. Human relevaflce ,udgment ofthe global pool. The other doqrments are assumed to b€ iftelevant. Evaluation of each system (wih 1000 answers)

. Partial relevance judgments

. But stable for system ranking

11

tl

Tracks (tasks)Hoc track: given document collection, different

topics. Routing (filtering): slable interests (user profile),

incoming document flowr CUR: Ad Hoc, but with querles in a different

language

. Web: a large set of Web pages

. Question-Answering: When did Nixon visit Orina?

. Interactive: put uf€rs into action with system

. Spoken document retrieval

. Image and video retsieval

. Information trackino: new tooic / follow uo

51212077

Some techniques toimprove IR effectiveness

. Interaction with user (relevance feedback)- Keyv{ords only cover part of the contents- User can help by indlcadng relevanvi.relevantdocument

. The use of relevance feedback. To lmprove query expression:

Q* = c!*qE + P*Rel-d - Y*Nrel-d

where Rd-d = c€rtrord of elevant doqrm€ntsItRel_d = cenuord of no.!rcle!?nt doorir€its

CLEF and NTCIR

. CLEF = Cross-Language ExperimentalForum. for European languages

. organized by Europeans

. Each per year (March - Oct.)

. NTC]R:. Organized by NII (Japan)

. For Asian lanquages

. cycle of 1.5 year

Impact of TREC

. Provide large collectlons for furtherexperiments

. Compare different slstems/techniques onrealistic data

. Develop new methodology for systemevaluation

. Similar experiments are organized in otherareas (NLP, Machine translation,Summarization,...)

t2

Effect of RF

Query expansion. A query contains part of the imporbnt words. Add new (related) terms into the query

. Manually constsucted knowledge base/thesaurus(e.9. Wordnd). Q = lnformalro.l ttti€tal, Q' = (nfom|6ton + data + l(lotil€dqe + . .)

(retne'/al + s€6rch + s€€king + ...). Corpus analysis:

. two terms that ofrefi @oaorr arc €lated (Iiutlallntormatbn)

. Two tE ms that coc.ur Wth th€ sarn€ words 6rtrelated (e.9. T-shid and co6t wlth wEar, . .)

Modified relevance feedback. Users usually do not cooperate (e.9.

Altavista in early years)

. Pseudo-relevance feedback (Blind RF). Using the top-ranked documents as if they

are relevant:. Seled m tenns from n top-ranked documents

. One can usually obtaln about 10% improvement

sl2l20t7

Global vs. local context analysis

. Globalanalysis: use the whole documentcollection to calculate term relationships

. Local analysis: use the query to retrieve asubset of documenE, then calculate termrelationships. Combine pseudo-relevance fu€dback and term co-

occurences. l'lore eftuctive than global analysls

13

Some current research topics:Go beyond keywords

are not perfect representatives of concepts

aaal. da6 !t l,ctulq amiture?. tad of pradiorl:

"ooe-.dm', \y$en' tsss Dnore lEn "00er6tin0,519sn"

. Suggested solution. Sense dlsamdguaoon (diftcult due !o f'€ L.t of ontext al

! Using coinpou.d terms (m comdete dilirary o,compound tems, varia0on ln form)

. Using noun phrasea (sy,tacdc paftems + sElstas)

. SUlla long way to 90

Theory...neMorks

. P(QID)D1 D2 03 Dm

t1 t3 14 tnK=-wcl c2 il c4---.-__/.,' cl

Inference a

. Language models

revision

sl2l20t7

Related applications:Informatlon filteringIR: changing queries on stable document collecgon

. IFi incoming document flow with stable interests(queries). yetm dedr:on (h sleid of o.dering doori€nts). Mvantage: the des.'lpt on of us€/s lnterest may he

imprcved unng relevarlce feedbad (the user ls more willing

. t ffidlty: adrun U'rc.noE b k€€p/iJrbre do.ur€flt

. The basic technlques us€d for IF are tl'€ sante a5 those forIR - Trc sides of the same cdn"

. How to describe the relevancerelation as a logical relation?

D => e. What are the properties of this

relation?

r How to combine uncertainty with alogical framework?

. The problem: What is relevance?

74

{ Loglcal models

-_.

IR for (semirstructureddocuments

. Using structura I information to assign weighBto keywords (Introduction, Conclusion, ...). Hlerardlcal lndexing

. Querying within some structure (search intitle, etc.). INE( experlments

. Using hyperlinks ln indexing and retrieval(e.9. Google)

PageRank in Google

pnt;i,=t-at*at Pl!)' 'i ctt,)

. Asslgn a nun'ert vdlue to each paEB

. Th€ more a p.ge ls lrffi to by lmpoGnt pa9€6, tne mo.e thsp69e ls knpo.tant

, dr damd4 factur (0.8s)

, t16fiy ogt€r crlterla I e.g, p.oxhlty of qu€ry words. '...intu m.to.r rd6/d ..." b.t!, ol.n '... Ltom.lro.r .. rane?l ..."

s1212017

*6f, In on the Web-TNo

stable document collection (splder,crawler)

. Invalld document, duplication, etc.

. Huge number of documents (partialcollection)

. Multimedia documents

. Great variation of document qualityr MulUlingual problem

lfinal remarks on IR

. IR is related to many areas:. N[r, AI, database, machlne leamlng, user

rnodeling.... library, Web, muhlmedia search, ...

. Relawely week theories

. Very strong tradition of experimenb

. Many remaining (and exciting) poblems

. Difficult area: Intuitive methods do notnecessarily improve effectiveness in practice

15

is IR difficult

. But how much it is rewardinq to Oain 10%improvement!

. Vocabularies misrnatching. Synonymy: e.g. car v.s. aut tnobile

. Qlreries are amblguous. they are partjal specificauonot user's need

. Content representabon may be inadequate andincomplete

. The user ls the ultimatejudqe, but we dont knowhow the judqe judges.. Ttt€ nobon of d€vancE 6 imDr€ose, conte{- ard usei.

dep€nd€flt

sl2120t7

16

Hierarchical Clustering

Adapted from Slides by PrabhakarRaghavan, Christopher Manning, Ray

N4ooney and Soumen Chakrabarti

Today's Topics

t Hierarchical ciuslering. Agglornerative clustering techniques

. Evaluation

. Term vs. document space clusteringr Multi-lingual docs. Feature selection. Labeling

"The Curse of Dimensionality"

. l ,/hy document clustering is diffcultr Vvhile clustering looks intuitive in 2 dimensions,

many of our applications involve 1 0,000 or moledimensions. . -

. High-dimensional spac€s look different, Th6 prcbobilily ol mndo.n poinis boing cld$ droF quic,{y

ss U!€ din'€rt3ionality 9ro*r, F!fflamors. randm pan ol vecl* aro all a'n63r

p€rpsndlorlar.

Hierarchical Clusterin

. Build a tree-based hierarchical taxonomy(dendrognm) lrcm a set of documents.

1

t One approach: recursive application of aparlitional clustering algorithm.

Dendogram : Hierarchical Clusterlng

. Clustering obtainedby cutting thedendrogram at adesired level: eachconnectedcomponenl forms acluster.

Hierarchical Clustering algorithms

. Agglome6tivE (bottom-up):. srarr wih 66€h clo@m6nI being a snglo c.lustBr., Ev€ntuslly all documsnb b€lonq io ii. 3am€ .lusl8r

. Dlvisivo (top"down):. starr li/ h all dccJm€nt3 !6long to iho .snr€ clus!B..

. Ev€nu€lly 6€dr nod€ fo.m3 a dusler on il3 own.

. Do€s nol require lhe numb€r ofclusters * in advanc€

. Needs a t€rminatiodrBadout condition

. flE fial mod€ 'n

borh AgCloffiaijvo and OivisiE E ol m us€

Dendrogram: Document Example

. As clusters agglomorare, docs likely to fall into ahierarchy of 'topics' or concepts.

ad3v-

,iforo

Hierarchical Agglomerative Clustering(HAC) Algorithm

-

Start with all instances in their own clustetUntil there is only one cluster:

Among the curent clusters, determine the twocluste$, c, and c./, that are most similar.

Replace c, ard c/ with a sirgle cluster c, \, c,

2

Key notion: c/uster resentative

. We want a notion of a representative point in acluster, to represent the location of each cluster

' Representative should be some sort ot "typical' orcentralpoint in the cluster. e.9.,. point inducing smallest radiito docs in duster. smallest squared distences, etc.

. point that is the 'average' of all docs in the clustor. C€nlroid or c6nior of gr€vily

. M6asur6 inlarclusiddistan@! by disran@s of @nkoidJ

Example: n=6, k=3, closest pair ofcentroids

JS

Centroid aftercond step

Centroid aier 6rst step.

Outliers in centroid computation

. Can ignore outliers when computing centroid.

. What is an outlier?. Lots of statbtical defnitions, e-9.! aorerl ol poirt lo c€riroid > M r .on€ clusts rpneaa

IS.y 10.

a

a ctuuora

Closest pair of clusters

. l\,lany variants to defining closest pair of clusters

. Singls-llnk. Similadty ofthe /rosl cosine-similar lsingle-link)

. Complote-llnk. Similarity of tho 'turthesl' points, the /easl @sin+

similar

r Centrold. ClusteB whose centroids (clnters of grsvity) are

the mosl cosine-similar

. Average-llnk. Averege cosine between pairs ofelements

)J

Single Link AgglomerativeClustering

. Use maximum similarity of pairs:

sim(c,.c ,) = max sim(x,y)' rect.yec l

. Can result in "straggly' (long and thin)clusters due to chaining effect.

. After merging c, and c/ the similarity of theresulting clusler to another cluster. ca, is:

sim((c, w c,),c 1) = max(sim(c,, c,),sim(c,,c o))

Single Link Example

Complete Link Example

4

Complete Link AgglomerativeClustering

. Use minimum similarity of pairs:

sim(c,,c ,) = min srz(x,y).xEr,yec I. Makes "tighter,' spherical clusters that are

typically preterable.

. Affer merging c/ and cl, the similarity ol theresulting clusler to another cluster. cr. is:

sim((c, w c,),c;) = min(sim(c,, c r),sim(c.,, c r))

*> (8,

Computational Complexity

. ln the first iteration, all HAC melhods need tocompute similarity of all pairs oL individualinstances which is O(r,2).

. ln each ot the subsequent ,r-2 mergingiterations, compute the distance between themost recently created cluster and all otherexisting clusters.

r ln orderto maintain an overall O(rP)performance, computing similarity to each clustermust be done in constant time.. Else O(ri lo9 /,) or O(rr3) if done naively

roup verage s omerativeClustering

. U!6 everEgs similarity across all paiB within the msrgedcluster lo measurelhc aimilaity ol two duneB.

I-si,rrc.c I _ \ Ir,,,{j.i)c, ...,c rlr, r-,c. -tt,.2:...*.": , ,,,,Compromb€ b€ttxeen lrngle and comdelalink.

. AvdaS€d .c.oss arl od€r€d p6iu in U|€ m6g€d du6t6r

. Asag€d ovsr all psil. b€lB€€r u)€ lwo orilimlclu3lorsSorru pr6viou! \Noft ha! u!6d on6 ol th€36 oplions: som6 lhEolh€r. No cloa. dir€rcnc. in eflicscy

Efficiency: Medoid As ClusterRepresentative

. The centroid does not have to be a do@ment.

. Modoid: A cluster representative that is one ofthe documents

. For exampler the document closest to thecentroid

. One reason this is useful. Consider the representative ofa laEe cluster

(>1000 docum€nts). The centoid ofthis cluslerwll be a dense vector

. The madord oflhis cluster will be a spaEe vector

. Compare: mean/cent.oid vs. median/medoid

5

mp ng roup verageSimilarity

. Assume cosine similarity and normalized veclorswjth unit length.

. Always maintain sum of vectors in each duster.

.-I. .l=Sii.cr

. Compute similarity of clusters in constant time:

Efficiency: "Using approximations"

-

. ln standard algorithm, must find closest pair ofcentroids at each step

. Approximation: instead, find nearly closest pair

. use som6 data sttucture thal makes thisapproximation easier to maintain

. simplistic examplg: maintain clos€st pair bas€d ondistanc€s jn projection on a aandom line

Term vs. document space

. Cosine computation. Constanl lor docs in tem space. Grons linearly with corpus siz6 for t6rms in doc

spaca

. Clusler labeling. Cluste6 have clean descriptaons in terms ofnoun

phrase co-o@urence. Applicrtion of term clusters

Term vs. document space

. So far, we clustered docs bas€d on theirsimiladties in tem space

. Forsome applications, e.9., topic analysis forinducing navigation structures, can'dualize'. use docs as axes

. represent (some) terms as vectors

. proximity basad on co-occurenco ot terms in docs

. now clustering lerms, I,ol docs

Multi-lingual docs

-

. E.9., Canadian govemment docs.r Every doc in English and equivalent French.

. Must cluster by concepts rather than language

r Simplest pad docs in one language withdictionary equivalents in the other. lhus 6ach doc has a .opresentation in both

languages

. Axes are le.ms in both languages

6

Feature selection

. Which terms to use as axes for vector space?

. Large body of (ongoing) research

. IDF is a form ol feature seleclion. Can eraggerale noise e.9.. nis-spellings

. Better to use highest weight nid-fieguercywords- the most discriminating terms

. Pseudo-linguisticheuristiG, e.9.,. d.op stopwords. stemming/!€mmatization. use only noirns/noun phaeses

. Good clustering should 'figure ouf some ot the6e

How to Label Clusters

. Show titles of typical documents. Titles are easy to scan. Autho6 create them for quick scanningl

. 8ut yor.r can only show a few tiues whiafi may nolfully reprcsent cluster

. Show words/phrases prominent in cluster. More likely lofully represent cluster

. Use distinquishino v/oddphrases. Difsrstigllabslino

Major issue - labeling

. Afrer clustering algorithm finds clusters - how canthey be useful to the end wer?

. Need pithy labelfor each cluster. ln search results, say 'Anilnal' or'Caa in the

jaguar example. ln topic trees (Yahoo). need navigational cues.

, Oien dorE by hand, a posl*ro.j

Labeling

. Common heuristics - list 5-10 most frequentterms in the cenlroid vector.. Drop stop-words, stem.

. Differential labeling by frequent terms. Wthina colleclion 'Complters', clusteas all have

lhg word con prGr as frequent term.. Discriminant analysis of centroids.

r Perhaps better: distinctive noun phrase

7

What is a Good Clusterin

. lntemalciterion: A good clustering willproduce high quality clusters in which:. the intra-class (that is, inka-cluster)

similarity is high

. the inter-class similarity is low

. The measured quality of a clusteringdepends on both the documentrepresentation and the similarity measureused

External criteria for clustering quality

. Quality measured by its ability to discoversome or all of the hidden patterns or latentclasses in gold standard dala

. Assesses a clustering with respect tooround lruth

. Assume documents with C gold standardclasses, while our clustering algorithmsproduce K clusters, tI)1, uJ2, ..., UJK wilh n,members.

External Evaluation of Cluster Qual

. Simple measure: puritv, the ratiobetween the dominant class in thecluster rri and lhe size of cluster oji

IPuntY\a,1 = - 111v1a ,1n,, ) ieC

h,"

r Others are entropy of classes in clusters(or mutual information between classesand clusters)

Pu example

Chsedl CbisU

cluird l: Puity = l/6 (E r(t, 1, 0))' 5/6

cllnsn: l\lriiy= l/6(@r{r, 4, I))-.{/6

clu$d Itr Puity = V5 (tu{2, o, 3)) - 3/5

B

\-:/

,a\(..)

Rand lndex

Number ofpoints

Same Clusterin clustering

DifferentClusters inclustering

Same class inground truth A c

Differentclasses inground truth

B D

Rand index: symmetric version

A+DRI=A+B+C+D

Compare with standard Precision and Recall,

PA

RA

A+ B A+C

SKIP WHAT FOLLOWS

9

Rand lndex exam le: 0.68

Number ofpoints

Same Clusterin clustering

DifferentClusters inclustering

Same class inground truth 24

Differentclasses inground kuth

2NI

t-{)

Evaluation of clusteringPerhaps the most substantive issue in dalamining in general:

. how do vou measure ooodness?

Most measu.es focus on computalional effciency. Time and space

Forapplication of clustering to search:. M€asur€ retrioval effectiveness

Anecdotal evaluation

. Probably the commonest (and surely the essiest)r 'lwrote this clustedng algoriihm and look whal it

foundl'. No benchmarks, no comparison possible

. Any clustering algorithm will pick up the easystuff like partition by languages

. Generally, unclear scientilic value.

Approaches to evaluating

. Anecdotal

. User inspection

. Ground 'truth' comparison

. Purely quantjtatavg measures, Probabi&y of g6n6rating clusters found. Av€rago distan€ belween clusler mmbort

. Macroeconomic / utility

User inspection

. lnduce a set of clusters or a navigation tree

. Have subject matter experts evaluate the resullsand score them. some d6gr6e of subiectivity

. Often combined with search results clustering

. Nol clear how reproducible aqoss tests.

. Expensive /time-consuming

10

Ground "truth" comparison

. Take a union ot docs from a taxonomy & cluster. Yahoo!, OoP, newspaper sections ...

. Compare clustering results to baseline. e.9., 80yo ofth€ cllslers tuund map'cleanly' to

taxonomy nodas. Howwould w€ measure this?

. 8ut is it the'right" answer?&

. TherB can be severalequally righl answersr Forthe docs given, the static priortaxonomy may

be incomplete^/rong in places. iheclustering algorithm may have gotlon dght

lhings not in the static taxonomy

M icroeconomic viewpoint

. Anything - including clustering - is only as goodas the economic utility it provides

r For clustering: net economic gain produced by anapproaci (vs. snother approach)

. Strive Ior a concrete optimization problem

. Examples. recommendation systems. clock time for inleractive search

Ground truth comparison

r Divergent goals

. Static taxonomy designed to be the "right"navigation struclure. som6^/hat independent of corpus at hand

. Clusters Iound have to do with vagaries ol corpusr Also, docs put in a taxonomy node may not be

the most representative ones for that topic. cf Yahool

Evaluation example: Cluster retrieval

. Ad-hoc retrieval

. Cluster docs in retumed setr ldentify best cluster & only retrieve doc6 from it

. How do various clustering methods afiect thequality of what's retrieved?

. Concrete measure ofquality:. Precision as measured by userjudgements lor

these queries

. Done wilh TREC queries

11

Evaluation

Compare two lR algorithms. L send euery, pres€nt ranked results. 2. send quory, cluster results, pr6s6nt dusteG

Experimentwas simulated (no users). Rest/lts !€16 clustered into 5 dusters. Clusters w6re ranked according to p6rcsntag6

rol6vant doc!ments. Documents within clusteas were ranked according

to similarity to query

Relevance Dens of Clusters

I

.,]

I

'-i-III

Sim-Ranked vs. Cluster-Ranked

( l((x[l,lYf.ri].ra ll (iul6llt

;lag

"3{3.lld,2T6

a2a,llt I

-]:i2.2n

TlLL * ?leiria rl drlrl dd.r8.trl i,Itd LrL Ia 1&!e6{,.p r&orhll'n.

Buckshot Algorithm

-.

t Anolherwayto an eficiant implemantaUonr Yo!h.v.l. ciustar a e6mpl6, fr€n 85ign lh6 ontirc sel

Buckshor combin€s HAC 6nd K-tlr€6ns

FitEi Er'&rnly EJc a s€mplo ot inslaiL€B ol sizoaFRu.r g.oup{vorcgs HAC on lhi6 sampb, which

Ui€ the ralts ot H.AC 5a inilial s€(l3 lo. K-

Orcrax algorilhm is O(/,) and svoids probr.ro or

U!6 EAC lo b@ntr pK-D..o ,

t2

I I

{ts

Bisectino K-means

-

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13

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Code No: 5705?

HYDERABAD- 20t5

Mar. Mrks: 75

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JAWAHARLAL NEIIRU TECHNOLOGICAL UNIYERSITYB, Tech IV year I S€nester Errmitra,f"*, fVf".,ll"".

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KESHAV MEMORIAL INSTITUTE OF TECHNOLOGYNarayanguda, Hyderabad.

lV - B.Tech - I - Semester -Rl3 -l - Mid lnternal Examinations AUG - 2016

Sub: lnformation Retrieval systems Date: 1U08/16Branch / Section lT Duration : 60 Min, Max. Marks: 10

Answer any TWO from the following Questions

l: Explain about the following(Taxonomy level-2) CO-l

(i): Precision

(ii): recall

(iii): Inverted index table

2: Brief about probabilistic strategy in information retrieval (Taxonomy level-2) CO-1,2

3: Briefabout different relevance utilities. (Taxonomy level-2) CO-2

4: Briefabout any 5 clustering Techniques. (Taxonomy level-2) CO-3

l: Explain about the following.(Taxonomy level-2) CO-l

(i): Precision

(ii): recall

(iii): Inverted index table

2: Brief about probabilistic strategy in information retrieval (T,xonomy level-2) CO-1,2

3: Brief about different relevance utilities. (Taxonomy level-2) CO-2

4: Briefabout any 5 clustering Techniques. (Taxonomy level-2) CO-3

KESHAV MEMORIAL INSTITUTE OF TECHNOLOGYNarayanguda, Hyderabad.

lV - B.Tech - l- Semester -Rl3 -l - Mid lnternal Examinations AUG - 20'16

Sub: lnformation Retrieval Systems Date: 1U08/16Branch / Section lT Duration : 50 Min. Max. Marks: 10

Answer any TWO from the following Questions

IRS MID 1 KEY

2. The probabilistic model computes the similarity coefficient (SC) between a query and adocument as the probability that the document will be relevant to the query. This reduces therelevance ranking problem to an application of probabitity theory. Probability theory can be

used to compute a measure ofrelevance between a query and a document.a, Simple Term Weights.b. Non binary independent model.c. Language model.

3. Relevance Feedback-The top documents found by an initial query are identified as relevant.These documents are then examined. They may be deemed relevant either by manualintervention or by an assumption that the top n documents are relev4nt. Various techniquesare used to rank the terms. The top t terms from these documents are then added back to theoriginal query.Clustering-Documents or terms are clustered into groups either automatically or manually.The query is only matched against clusters that are deemed to contain relevant information.This limits the search space. The goal is to avoid non-relevant documents before the searcheven beginsN-grams-The query is partitioned into n-grams (overlapping or non-overlapping sequences ofn characters). These are used to match queries with the document. The goal is to obtain a"fizzier" match that would be resilient to misspellings or optical character recognition (OCR)enors. Also, n-grams are language independent.Thesauri-Thesauri are automatically generated from text or by manual methods. The key isnot only to generate the thesaurus, but to use it to expand either queries or documents toimprove retrieval.Regression Analysis- Statistical techniques are used to identify parameters that describecharacteristics of a match to a relevant document. These can then be used with a regressionanalysis to identifu the exact parameters that refine the similarity measure.

4. Single Link ClusteringThe similarity between two clusters is computed as the maximum similarity between any twodocuments in the two clusters, each initially from a separate cluster. Hence, if eightdocuments are in cluster A and ten are in cluster B, we compute the similarity ofA to B as

1. Precision is the ratio of the number of relevant documents retrieved to the total numberretrieved. Precision provides an indication of the quality of the answer set. However,

this does not consider the total number of relevant documents. A system might havegood precision by retrieving ten documents and finding that nine are relevant(a 0.9precision), but the total number of relevant documents also matters. lf there were onlynine relevant documents, the system would be a huge success.however if millions ofdocuments were relevant and desired, this would not be a good result set.Recall considers the total number of relevant documents; it is the ratio of the number ofrelevant documents retrieved to the total number of documents in the collection that are

believed to be relevant. computing the total number of relevant documents is non-trivial.

the maximum similarity between any of the eight documents in A and the ten documents inB.Complete Linkage

Inter-cluster similarity is computed as the minimum of the similarity between any documentsin the two clusters such that one document is from each cluster.K-MeansThe popular K-means algorithm is a partitioning algorithm that iteratively moves k centroidsuntil a termination condition is met. Typically, these centroids are initially chosen at random.Documents are assigned to the cluster corresponding to the nearest centroid. Each centroid is

then recomputed. The algorithm stops when the centroids move so slightly that they fallbelow a user-defined threshold or a required information gain is achieved for a giveniteration.Group AverageEach cluster member has a greater average similarity to the remaining members of thatcluster than to any other cluster. As a node is considered for a cluster its average similarity toall nodes in that cluster is computed. It is placed in the cluster as long as its average similarityis higher than its average similarity for any other cluster.

Code No: A7O533 Set No. IJAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

IV B.Tech. I Sem., I Mid-Term Examinations, August- 2O16INFORMATION RETIEVAL SYSTEMS

Objective Exam

HaIl Ticket No.

Answer All Questions. All Questions Carry Egual Marks. Time: 2O Min. Marks: 1O.

Choosc the correct alternative:I

l. Srnonlm ofclusler uscd in literature is

a) groLrp b)class c ) hierarch-r,. d)none

3. T'o improve run-timc perlonnance of clustering algorithm _Irl test search b) document search c) digital array processor d)none

A

2. Il'the documcnts arc identified as possible relcvant to the query. it is best to present more likely relevantclocrrrnents llrst is callcd as .ta a)srnoothinq b)ranking c)clustering d)retrieving I 19 l

is used

tclL the rrost conrrlonly used sinrilaritl' measure isa)dicc b) jacctrds c) cosine d) singhal '

5) Probabiliq of maximum likelihood of terms in the document rnodel is given as

r) onlv term t'reiluencv b) ratio of term frequency to document length c) ratio ofterm frequency to documentr.errn liequencv d)onl1'document tcrm frequency t C ]

(r. I-he mcthod that is resilient to misspelling is

a) parsing b) N-grams c) Regression analysis d) relevance feedback t 3l7 \\ hich tcchni(lue uses onll Olkn tbr clustering is

r) onc pass b) k-means c) buck shot d)rocchioo I C I

8 'l'hc lbllori ing rnethod attempts to group docurnents by conient to reduce the search space required torcspond to a qLrcrr..\) ( lur,ierinq B) Spooling C) Rclevance lccdback D) Hicrarchical clusterirg t A I

9. Dtrla stl1rctrrlc used to fcprcsent the docurnent is

lu)irrr crtcd indcx tablc [r) stack b) queuc c) linked list A

10. ) Clustering is used to increase _by expanding searches with related tennsrr) prccision b)recall c)a&b d)none lCl

c

AI

I

Name:

Code No: A7O533

II I'IIL IN TIIL I]I,.\NKS

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+

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KESHAV MEMORIAI INSTITUTE OF TECHNOLOGYNarayanguda, Hyderabad.

lV . B.Tech - | - Semester -Rl3 -ll . Mid lntemal Examinations Nov. - 2016

Sub: lnformation Retrieval systems oate: 08/11/16 - FN

Branch / Section lT Duration:60Min. Max.Marks:lo

Answer any Two from the following Questions

1. Brief about cross language information retrieval and the ways in which they can be

translated. (Taxonomy Level-2) co-s

2. Write about signature files and duplicate document detection. (Taxonomy Level-2) Co-3

3. Define an inverted index and how do we build an inverted index. (Taxonomy Level-l) co-4

4. Brief semi structured search using relational schema (Taxonomy LeveF2) CO-5

IRS MID 2 KEY

ttCross-Language lnformation Retrieval (CUR) is quickly becoming a mature area in the

information retrieval world. The goal is to allow a user to issue a query in language L and

have that query retrieve documents in language L'. The idea is that the user wants to issue a

single query against a document collection that contains documents in a myriad of

languages. An implicit assumption is that the user understands results obtained in multiple

languages. lf this is not the case, it is necessary for the retrieval system to translate the

selected foreign language documents into a language that the user can understand.

Numerous resources are needed to implement cross-language retrieval systems. Most

approaches use bilingual term lists, term dictionaries, a comparable corpus or a parallel

corpus. A comporoble corpus is a collection of documents in language L and another

collection about the same topic in language L'. The key here is that the documents happen

to have been written in different languages, but the documents are not literal translations

of each other. A news article in language L by a newspaper in a country whlch speaks

language L and an article in language t'by a newspaper in a country which speaks language

L'is an example of comparable documents. The two newspapers wrote their own article;

they with comparable corpora are that they must be obout th e some topic. A book in French

on medicine and a book in Spanish on law are not comparable, lf both books are about

medicine or about law they are comparable. We will discuss CUR techniques using a

comparable corpus. A porollel corpus provides documents in one language L that are then

direct translations of language L' or vice versa.

2. Signature Files

The use of signature files lies between a sequential scan of the original text and theconstruction of an inverted index. A signature is an encoding of a document. The idea is toencode all documents as relatively small signatures. Once this isdone, the signatures can bescanned instead of the entire documents. Typically, signatures do not uniquely represent a

document,so it is usually necessary to implement a retrieval in twophases. The first phase

scans all ofthe signatures and identifies possible hits, and the second phase scans the originaltext of the documents in the possible hit list to ensure that they are correct matches. Hence,signature files are combined with pattem matching. Figure?? illustrates the mapping ofdocuments onto the signatures. Construction of a signature is often done with differenthashing functions. One or more hashing functions are applied to each word in the document.

Duplicate Document Detection

A method to improve both efficiency and effectiveness ofan information retrieval system is

to remove duplicates or near duplicates. Duplicates can be removed either at the timedocuments are added to an inverted index or upon retrieving the results of a query. Thedifficulty is that we do not simply wish to remove exact duplicates, we may well be

interested in removing zear duplicates as well. However, we do not wish our threshold fornearness to be so broad that documents are deemed duplicate when, in fact, they are

sufficiently different that the user would have prefered to see each of them as individualdocuments. For Web search, the duplicate document problem is particularly acute. A search

for the term apache might yield numerous copies of Web pages about, the Web serverproduct and numerous duplicates about the lndian tribe. The user should only be shown twohyperlinks, but instead is shown thousands. Additionally, these redundant pages can affectterm and document weighting schemes. Additionally, they can increase indexing time and

reduce search efficiency.

3. Inverted Index

lndexing requires additional overhead since the entire collection is scanned and substantiall/o is required to generate an efficiently represented inverted index for use in secondary

storage. lndexing was shown to dramatically reduce the amount of l/O required to satisfy an

ad hoc query. Upon receiving a query, the index is consulted, the corresponding posting lists

are retrieved, and the algorithm ranks the documents based on the contents of the posting

lists. The size of the index is another concern. Many indexes can be equal to the size of theoriginal text.

4. Semi-Structured Search using a Relational Schema

Numerous proprietary approaches exist for searching extensible Markup Language (XML)

documents, but these lack the ability to integrate with other structured or unstructureddata. Relational systems have been used to support XML by building a separate relationalschema to map to a particular XML schema or DTD (Document-type Definitions) .

Code No:

Name:

A70533 Set No. IJAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

lV B.Tech. I Sem., ll Mid-Term Examinations, November- 2016INFORMATION RETIEVAL SYSTEMS

Objective Exam

Hall Ticket No.

AnswerAll Questions. All Questions Carry Equal Marks. Time: 20 Min. Marks: 10.

I Choose the correct altErnative:

l. NLP is the abbreviation for t Dla) Neutral Language Processing b) Natural Language Processing c) Natural Language Prioritizing d)none

2. The method that normalizes the term by eliminating the suffixes and prefixesa) Thesauri b)Scanning c)Stemming d)Stop words tg3.Compression ofthe index results in a space requirement ofless then _ ofthe original texta)s\% b)60% c)44.5Yo d)10% I ) |

4.An inverted index includes number of componentsa)Five b)Two c) Three d)Four r)s) - is an encodirg ofa documenta)Signature b)Duplicate detection c)Iiverted Index d)Index Compression At6.Hashing scheme which utilizes only some terms in a documentis used bya) Index Purning b)Signature files c)Query processing d) I-Match 9t

l

7. The database system model consists ofa)inverted list b)hirarcheal network c)relational model d)All the above

8 are used for identifing people names,organizations and locationsa)POS b)Named entity taggers c) Named equity taggers d)none

9.

t-D

t B

file includes the actual posting list for every distinct term in the collection^ a) index b)weight b) document c)signature rAl

l0.Efficiency and effectiveness ofan information retrieval system can be removed by removinga) Duplicates b)Enors c) collection d)none t A l

Code No: A70533 Set No. 'l

II FIIL IN THE BLANKS

I l. lnverted index compression is generally used to speed up /Ll P.<,octst t'1

12. A static index pruning is a Lo9''/ approach.

l3 consists of huge quantities of structured information.

14. fuA< )el&)iol,generally performed by determining a unique hash

value for every document.

15. The list that contains the terms that are not meaningful with respect to document

)ec

relevancy 1S wn{l

16. Syntactic parsing is employed for phrases

ooiqtf file includes the weight for each document.

l8 ptrolu'ttt retrieval is operated using regular operators AND,OR,

NOT.

9op

17

19. CLIR stands for C.rosr l.an tt4 el^lt,^*lio n \e-l"t'cvtl

20. Bilingual term list is the resource used by (pa

-000-

IRS Assignment 2

1. Define a inverted index and how do we build a inverted index. (Taxonomy Level-2)

2. Explain in detail information retrieval as a relational application. (Taxonomy Level-2)

3. Write short notes on duplicate document detection. (Taxonomy Level-2)

4. Explain about the concept of signature files. (Taxonomy Level-2)

5. Describe about semantic networks. (Taxonomy Level-2)


DEPARTMENT OF IT

TUTORIAL TOPICS

SUB: IRS.YEAR/SEM: IVflCLASS: IT

TUTORIALNUMBER

TOPIC DESCRIPTION

1

I

16l6/2016 Vector Space Model Understand VSMand its use in IRS

2 23/6/2016 Example on the concept

Simple term weights.

Working withdocuments and

query to

understand the

concept

3 30l6/2016 Retrieval utilities Briefing about allthe differentutilities

2

7 /7 /2076 N-Grams Use of N-Grams

and working withthem

) t4/7 /2016 Thesuari Define thesaurus

and the use of it6

3

z8/7 /20!6 CLIR

7

4

78/8/16

2s/8/20!6 Signature Files Use of signature

files in IR

9

5

8/9/20!6 lR as a relational

application

Explain the

concept and

working

i0. ts/9/2016 Unit briefing A briefing of allthe topics

22/9/2076 Web Search Define the

concept of web

search

UNITNUMBER

DATE

4.

Difficulty ofworking withmultiplelanguages ininformationretrieval

lnverted index Different ways ofcreating indexes

8.

1l


DEPARTMENT OF INFORMATION TECIINOLOGY

Name of the Faculty: Dr.Ramakanth Mohanty

Course: B.Tech IVA

StNo

Roll No Name of the student

StudentperformanccuptopreviousS€m€ster

Studentperformance based onInt€rnalexam marks

OveralIGrade

I 13801A1201 B

AVADHANAMSUBHAKEERTHI

AI

138D'1A1203 B SRA\,YA c c B c

I 138D141204 B SRINIVAS B B B B

5 138D141205 BADHURI NIKHIL SAI B B B

6 13BD'1A1206 BODLA SAI KRISHNA B B

138D141207 BYALYA PHANI RAJ B B B

3 138D1A1208 CHAITANYA THAKUR B

9 13BD1A1209 CHIIVALAPATI SRIKAR c c B c

l0 138D141210 D AKHIL B B B

I1 138D141211 DODLAPATI SHAILAJA B A

t2 138D1I.1212 G NIDHI RAO B B A B

l3 138D1A1213 G SANJAY c B c C

l4 13BD'lA.1214 GOPAL HULSURE B B B

l5 138D1A1215 GOUNI TEJASWINI B c c C

l6 '138D,141216 GUNDETI ANITHA B B B

11 138D141217 HEN/A NEEHARIKA P A

Subject: IRS

BI

Stud€ntperformanc€onprerequisitecourse ifany

A THODESHWARI A

2 1380141202

B

c

B

B B B

B

JANARDHAN DESAI c B c C

K KESAVA KAUSHIK

20 138O1A'1220 K SIMON JOSEPH B B A B

21 138D1A1222 KASHI SRAVAN B B

13BO't A1?23KOTAMARTHYABIJITH KISHAN

C B cI

C

138O141224M D ABDUL HAFEEZSHAREEF

c BB

B

24 138D141225 M SANDEEP B B B

138D141226MANDAVALLI DURGALAVANYA

B A

26 1380141227 IVANGU TEJASWINI c B B B

2'7 138O141228IVEELA PAVANKUI\,4AR

B A

28 138D1A1229 I\4OYYA ANEESHA B B B B

29 13BD1A1230 N SANDHYA A

30 138D141231NADARGULU SAI

RAGHAVAB A A

3I 138D1A1232 NIYATI SHAH B A I

32 138D1A',|234 PANDILLA DIVYA A B B B

PATLURI SUSMITHAPRIYADARSHINI

B BB

B

14 138D1A1236 PEDDI SOUMYA B c A B

35 138D1A1237 PIYUSHI V V B B B B

i6 138D1A1238PRAHARSHITAKRISHNA

B

37 138D141239PURAMHARITHKUMAR

c B c c

38 138D1A1240 c B c c

39 '138D1A.1241 RAHUL PATIL B B B

40 138D1A1242 RANGU SUPRIYA B B B

18 |

13BD1A1218

re I

13BDl4121e B

22

23

13801A1235

I

I

R SUSHEEL

B

I

B

B A A

42 138D1A1244 S PRIYANKA B C B B

43 138O141245 S SACHIT REDDY B c B

44 138D141246 SAGI KAUI\4UDI B B A

45 1380141247 SAINI DIVYA SREE B B

46 13801A1248SAMUDRALA LAXMISINDHU

B A B

4',7 138D141249 B c B

48 13B01A1250 SONIA JAISWAL B B

19 1380141251 SRI RAM SAI TEJA B A B

50 138D141252 SRIYASRI A C B B B

51 13BD141253 TEDDU POOJA C B c c

52. 138D141254 U PPALAPATI HARIKA B c B B

53 138D141255 V ANUSHA B B A B

c c54 13B0141256 B c

55 138O1A1257B B

CB

56 138D1A1258 VINAYAK I\,4AITREYEE B B A

51 138D141259 YADLA YESHASWI B B B B

5E 138D1A1260 YALLA AKHIL B B

Note: Grade individual student accordingly

A : Advanced Leamer (>75Yo)

B : Average Leamer (50 to 75% )

C ; Poor Leamer (<50%)

4r 1138D1A1243 |

necoruoa enrvnrurn

B

B

B

SODARI SHIREESHA

c

IB

I

V GREESH[,4A

VANAM SRAVANPARASAR

B

B


DEPARTMENT OF INFORMATION TECHNOLOGY

Name of the Faculty: Dr.Ramakanth Mohanty

Course: B.Tech IVA

Subject: IRS

SINo

Roll No Name of the student

StudentperformanceuptopreviousSemester

Studentperformanceonprerequisitecourse ifany

Studentperformance based onExternalexam marks

OveralIGrade

t 138D141201 A THODESHWARI B

138D,1A1202AVADHANA[,,ISUBHAKEERTHI

AB

138D1A1203 B SRAVYA c C AB c

I 138D1A1204 B SRINIVAS B B B B

5 1380141205 BADHURI NIKHIL SAI B B B B

6 138D1A1206 BODLA SAI KRISHNA B C B I}

1 13801A1207 BYALYA PHANI RAJ B B B

I 138D1A1208 CHAITANYA THAKUR B B c B

I 138D,141209 C (- C

l0 138D1A1210 O AKHIL B B B

ll '138D1A1211 OODLAPATI SHAILAJA

I2 138D1{',t212 G NIDHI RAO B B B B

t3 G SANJAY C B B

t4 GOPAL HULSURE A B B

138D141215 GOUNI TEJASWINI B c L c

t6 138D141216 GUNDETI ANITHA A B (- B

t7 138D1A1217 HEMA NEEHARIKA P B B

l3 '1380'1A1218 JANARDHAN DESAI C B c c

t9 138D141219 K KESAVA KAUSHIK c

2tl 138D1A1220 K SIMON JOSEPH B B B

I

CCHTMALAPAT SRTKAR I

B

B138D1A1213

B138D1A1214

l5

B

2l 1380141222 KASHI SRAVAN B A

22 138D1A'1223KOTAMARTHYABIJITH KISHAN

C B

cC

23 138D141224M O ABDUL HAFEEZSHAREEF

C B

C

C

138D141225 M SANDEEP B B B

25 1380141226MANDAVALLI DURGALAVANYA

B

26 138D141227 MANGU TEJASWINI c B c C

21 138D141228MEELA PAVANKUMAR

B

28 MOYYA ANEESHA B c B

N SANDHYA A A

30 13BD'1A1231NADARGULU SAI

RAGHAVAB

B

B

3l 1380141232 NIYATI SHAH B B B

12 138D'1A1234 PANDILLA DIVYA B C

l3 138D'1A1235PATLURI SUSMITHAPRIYADARSHINI

B B B

34 '138D1A1236 PEDDI SOUMYA B C B

35 138D1A1237 PIYUSHI V V B B c B

36 138D1A1238PRAHARSHITAKRISHNA C

B

37 138D1A'1239PURAMHARITHKUMAR

C B B

138D1A1240 c B B

39 138D'1A1241 RAHUL PATIL B A

40 138D141242 RANGU SUPRIYA B B B

4l 13BDlA.l243 REGONDA PRIYANKA B B B

13BDlA't244 S PRIYANKA R C B

4l 138D1A1245 S SACHIT REDDY B C B

41 138D1A1246 SAGI KAUMUDI B B B

45 138D141247 SAINI DIVYA SREE B

138D141229 B

2e 138D141230

B

38 R SUSHEEL

B

I

46 138D1A1248SAMUDRALA LAXMISINDHU

B

4'l 138D141249 SODARI SHIREESHA B B B

.18 138D141250 SONIA JAISWAL B c B

19 138D141251 SRI RAM SAI TEJA B B B

50 138D1A1252 SRIYASRI A C B c C

5l 13BDlA12s3 TEDDU POOJA C B c c

52 138D141254 UPPALAPATI HARIKA B c c C

53 138D1A1255 V ANUSHA B B B R

54 13BD,1A1256 V GREESHMA B c c

B Bc

B

56 13BDlA'1258 VINAYAK MAITREYEE B B B B

57 138D,1A1259 YADLA YESHASWI B B c B

58 138D1A1260 YALLA AKHIL B D B

Note: Grade individual student accordingly

A : Advanced Leamer (>75V)

B : Average Leamer (50 to 151:o)

C : Poor Leamer (<50%)

I

BlI

I

c

ss I reeo*rzsz lyl|iy^:fl*-tl

Keshav Memorial Institute Of TechnologvDepartment of Information Technology

COUrsqOutcome AttainmentName of the faculty :

Branch & Section:

Subject:

Dr Ramakantha Mohanty

ITIRS

Academic Year 2016-17

Exam: I Intemal

IV Semester: IYear:

5

Question No.

1A IB tc 2A 2B 2C 3A 3C 4A 4B 4C

objl AI

Max. Marks :>

S.No HT No.

5 5 5 l0 fr 138D1A1201 4 5

42 138D141202 . 4

4 2

4 3 4

5 4 3

6 5 4

7 5 3

8 138D1A1208 4 5

4e 13BD141209 3

l0 5

5r1 5

4 512

t3 3

4 4t4l5l6 3 4

17 138D1A1217 5 5

Ir8 138D1A1218 -19 138D141219 l 3

420 432t 2

2.522 2.5

423 138D1A1224 4

4 424

2-r 138D1A1226 55

4 426527 4

428 4

529 5

430 5

43l 4

4 5

33 3 4

534 5435 5

4i6 '138D 1A1238337 138D1A1239

1i8 138D141240539 4

IIIIIIIIIIIITIIIIIIII

IIIIIIIIIIIIIII

IIIIIIIIIIIIIITIIIIIIIIIIIIIIIIIIIIIIIIIIITIIIIIIIIIIIII

IIIII

I IIII IIIII

II

IIIIIIIIIIIIIIIIIIIIIIITIIII IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

IIIIIIIIIIIIIIII

IIIIIIIIIIIIIIIIIITITIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIITIIIIIIIIIIIIIIIIIIIIIIIII II I

3B

I

7

8.553 138D141203

138D1412046 -:,' 5

13BD141206138D1A1207

656565138D1A1210

.138D1.A1211

138O1'A1212 75138D1A1213

65138D1A121413BD1A1215

.5. ..,:,, 5138D1A1216

65138O1A.1220138D141222

657.5138D141225

5

65138D1A1227138D1A1228138D1412?9'138D1.A1230

75138D1A1231651384141232

,138D1A1234138D141235

51.38D1A1237

13801{',t241

tt

----r----

138O141236'

40 1242

AVERAGE

CO NIa ln with Exam estiotrs

Students Scored >Target

vo 18 60 60 4 60 60 ) 60 60 60 60 39 58

% Students Scored>Tuget o/o 87% t00% 67v, 920/. 680/o 100%

CO Attainment based on Exam uestionsi

A.ttairlment Leye

1 <40yo

40-600/.

>600/.

l 4

3 44t '138D1A1243 3

43

44l t1i3BD1A12

12.13BDlA'1245,4

41.1 138D1A1246 4

445 .:138D1A1247::'. ' 4

546 ''38D1A1248 )47 :t13BDtA1249t' : j

518 138O141250 4

49 l.1i38D1]A12511.',. 4

50 138D1A1252 l 4

5l .138D141253. r 2

5l 138D1A1254 l 3

53 r,138D1A1255_ 5 4

54 ri13BD1A1256' 2

s5..1.38D141257s6 ,.138D1A1258 .1. s 4.5

57 138D141259 3

58 4

i9BD1iA1 4

60

ST \I 202 0 16 0 0 8 0 194 0 0 337 290

COUNT 55 0 0 4 0 0 0 0 49 0 0 58

3.6727 4 2.67 3.96 5.9123 )

II

rII

II

II IIIIII

IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

co-l Y Y Yco-2 Y Y Yco-3 Y v vco-4co-5

IIIIIIIIIIIII

IIIII

co-l 100% 100v"co-2 81% l00Yo 67v. 68% 100v.co-3 92v.co-4co-s

IIII

IIIIIIII III Ico Subj obj Asgn Overall

co-1 t00% 680/. 100% 89%

co-2 85% 680/. 100% 84vo

co-3 92yo 680/0 1000/o 8',7vo

co-4

co-sIII

6, 5

55

5

75

6. ,5

l

7' ''54 55

6 :i:15

0 0

5',1

Ir

r

I

f--r-

I

tt

tl

tt

ffi

=I

F

---T--

E

I

ttttttmH ttttt

45

68Yo

680/0 100%

m ttlt

ltttttttr--r--

Level

t:

Overall Course Attainment - 3

D€partment of Information Technology

Course Outcome AttainmentName of the faculty : Dr.Mohanty

Branch & Section: ITSubject: IRS

Academic Year 2016- 17

Exam: II Intemal

IV Semester: IYear:

Question No. objl A1

IB IC 2A 2B 2C 3A 3B 3C 4A 4B 4CIA10 5Max. Marks :>

S.No HT No.

5 5 5

4 4 3I

52 4

I 3

4 24 138D1412044 2s '138D1A1205'

46 2

1 138D141207 3 1

48 138D14120839 .'138D141209510.,138D141210

11 ,.138D141211 . 5 4

4 4t2 ,1384:,141212 .,

213 2

14 I 4

15 4

216 ,138D1A12:16 .,,.

5t'7 43

5

20 l 4 4

4 7322

23 2 3

21 .138D141225 . 4

5 4

26 4 3

_ t3t7utAttl6 4.5 4.5 I28 138D141229 J 3

29 13BD141230 4

30 5 4

31 138D141232 4

32 13BD1l\1234 2 4

33 4

534 4

35 4

36 4 3

3'7 7 L

2 L

39 .138D141241 .: 2

40 .138D1F'1242 4

41 4.5 4.5

42 4 4

IIIIIIIIII

IIIIIrIIIIIIIIT

IIIIIIIITI

IIIIIIIIIIII

IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

II

III

IT II

I

II

IIII

II

IIIIII

IIIIIIIIIIII

IIIIII

IIIII

IIIIIITIIIII

IIIIIIIIII

IIII

rIIrIIIIIIII IIIIIIIIIIII

IIIIIIIIIIIIIIIIIIIIIIITIIII

IIrIIIII

IIII

IIIIIIII

II25

5

75138D,1A12017-5'138U1A1202

45138D1A1203654,5bc,'l3BDl41206 -

753

5 .,5

75.6 .' ,.5

5 .,,5l3BDl 41213.,,..138D1N1214 . 5

6 ,513BD141215..5.5

:138D1A1217 .. 9. ,. ..5

ocl8 IJr]UIAIZIO;13BD1A1219.l9,.138D1Aj224.

2t 138D1A1222,138D1A.1223.138D141224 .

q6

45138D141226138D1A1227

75

5 75138D1A1231

8565

138D1A1235

'13BD1A1237 4 -' -5

:138D141238,:r13BD1A'1239 4 "' 'r'5

-l

'13BDl A'1240 ,i'

3

.138D141243.r,.

138D1A1244:.

r t-rr-r

L

--r-r f-r-

ffill

ttt I

+ LLi+ rt---T_--r--T-r

L

,13.138D1A1245,. 3

CO Ma tn with E m n

coco-1

Su bj obj Asgn Overall

co-2

co-3 84o/o 100% 84% J

co-4 79% 84% t00% 88%

co-5 64o/o 84% 100% 83%

5.E

,,5

0

Students Scored>Target Yo 60 60 8 60 60 J6 60 60 '7 60 60 49 58

%o Students Scored>Target Yo 74% 62% 79% 64% r 00%

me ed on Exam uestions:

{.ttainment Leve

I <40Yo

2

3 >6j%o

4

44 i13BD1A124A 4 4

+t tSllutl\t24t 4 4

16 '13BD141248 4.5

41 138D1A1249 2 1,

as r13s[fi12ifl 3

49 ,138D1A1251:: .1 4 7

50 ,'13.8D1.A1252,. 4 4

ir 1 1 3 1

52 13BDlA1254 2 2

5i 138D1Ai255 4 4

54 r138D1A1256 3

5s 138D141257. 2 1

56 ,138D1A1258. .1 2 3

57 .13BD1A1259.. 2 3

2.l

59

3F8f, D 1I

60

SUM I 19 0 JO 0 164 33.5 0 0 330 290

COUNT 35 0 0 l3 0 0 48 0 0 lt 0 58

AVERAGE 3.4 2.'77 3.05 5.6897 5

IIIIIIIIIII

IIIIIIIIIIIIIIIIII

IIIIIIIIIIII

IIII

II

IIII

co-lco -2co-3 Y v vco-4 Y Y Yco-5 Y Y v

co-1co -2co-3 620/0 1007o

co-4 79% 840/o t000/0

84% 100%co-s 64%

IIIIIIIIIIIII

Overall Course Attainment = 3

3.:.5

6,5

65

0 0 0 0

0 58

3.41

tr-

=

II

I r t-

I

tt

tt

+

+-+

--+

Ytttttttt

I

----T---T---r--r

ttl

rT-T---r--rttt+ LI III

26

84o/o

'74% 84o/o

f---r--l---T_--r LIttt I -#Level

68%

f--T---f-r--40-600/o

Name of the faculty :

Branch & Section:

Subject:

Department of Information Technology

Course Outcome AttainmentDr.Mohanty

ITIRS

Academic Year: 2016-17

Exam: University

Year: IV Semester: I

SL.No REG. NONAME OF TIIE STUDENT

)lax Nlarks 75.00

138D1A1201 501 138D141202 AVADHA\,A]\1 SUBII,\ K EERTIII 41

3 138D1A1203 B SRAVYA AB4 138D1A1204 31

5 t,lan,taarna I',\DHl'RI \1KHIL S \I6 138D1A1206 BODLA SAI KRISHNA 437 1t1PD1A1207 BYALYA P] IANI..RAJ . ,:,: . 268 1'38D1A1208 CIIAITA\\ A fHA KUII 299 138D141209 CH1I'{ALAPATI SRIKAR 10

l0 138D'1A12:10

'138D1A12'11 F a\nl . t) \ rt(u]\ Al-1 5012 13BD141212 C NIDHI RAO 41

l3 13ED1A1213 4314 138D'1A'1214 COPAI'H ULSLIRE 44l5 13BD141215 COLT.{I TEJAS\\TINI '16

138D1A12161'l 138D1A1217 HEMA NEEHARIKA P 38

13 '138D141218 JANARDHAN DESAI 1

l9 13BD1A121S K KESA\'\ KAUSHIK 32

20 lseot erz2a 50

?\ 13E,D1A1222 KASHI SRAVAN 48

138D1A1223 KOTANlAR I HY ABIJITI'I KISH,\N 12

23 fiF,?1pI224 \I D ABDI I,, H,\TTI,Z SHAREFF 17

21 138D141225 ]U SANDEEP I 4625 13BD1A'!226 MANIiAVALLI.]DURCA L. AV'ANYA..Ii]::, 5426 138O1A'1227

138D1A1228 I\.IEELA PAVAN KUi\,lAR 51

1!iBD1pn22a N4OLY,A ANEESHA ,,:':i.:., ,.,,l::,,,. . JI29 138D1A1230 51

30 138D141231 NADARGULU SAI RAGHAVA 46

-138D1A1232. NIYAtlr SHA |'l,,lll:lrt,: .,,,rill,:,i,... , ri,ll,r ,, 46

TOTAL

I A THODESI.IWARI

B SRNIVAS

D AKHII,ll

G SANJAY

t6 GLNDETI ANITHA

K STMON ]OSEPF{

22

MANGU TEJASWINI27

N SANDHYA

32 138D1A1234 JZ

33

31

35

36 JZ

37 49

38 47

4941 4742 54

43 138D1 45

138D141246 4945 I.JtlLJll I 21l , 57

5b

4848 138D1A125( 4449 138D1A1251 3750

51 0

52 2953 tJElU t/\ tzcc 4051 138D1A1256 2855 138D141257 18

56 '138D1A1258:rla

57 2758

59

60

sum

avg

2156

39.2

no. ofstudents scored more than target 70 32

no. ofstudents present 57

Percentage of students scored morc than target 7o 56%

Attainment level 2

Attainnrent Le\ Percentage

40-60%

PANDILLA DIVYA1380141235 PATLURI SUSIVIII}IA PRIYADARSHINI

138D,1A1236 PEDDI SOUMYA

1 3 B D'1 1\'t 237li

138D141238: :PRAHARSHITA:'KRISHNA

13BDlAt239 PURAM HARITHKUMAR13BD 1A1240 R SUSHEEL

39 138D1A1241 RAHUL PATIL40 13BD1A1242 RANCU SUPRIYA]

13BD1A12A3]:.(Ecornapniii.riNre

-',rt.l:'lttait:::l'l

138D1A1244 S PRIYANKA

S SACHIT REDDY

SAGI IIAUMUDISAINI DIVYA 6RE.E

46 138D141248:: SAMUDRALA LAXMI SINDHU47 13BD1A'1249. SODARI SHIREESHA

SONIA JAISWAL

SRI RAM SAI TEJA

138D141252 SRIYASRI A138D141253 TEDDU POOJA,::r:l:lrirrrrr:,1

13BDlA r 2&:l UPPALAPA,Ii HARIIiA

V ANUSHA

V CREESHMA

VANAM SRAVAN. PARASAR

VINAYAK MAITREYEE

13BDlA1259,l Y]ADI-A YESHASTiiIii

138D,1A1260 YALLA i\KHIL

t l.qov"

3 l-eo.."

Department of Information Technolog-v

Course Outcome AttainmentName of the faculty :

Branch & Section:

Subject:

Dr.Mohanty

IT

Year: IV

Academic Year 2016-l 7

Exam Overall

Semester: I

2.3

Course Out(lst InternalExam

2nd InternalExam

3rd InternalExam

UniversityExam

co1 3.00 2

co2 3.00 2

c03 3.00 2

co4 2

2cos

Attainment level of Course Outcomes

Average

Overall course attainment level 2

Course OutcomesAttainment

Level

co1Use history of information retrieval research for development of

information retrieval systems. 2.3

co2Explain core concepts and terms of information retrieval. 2.25

co3Understand the difficulty ofrepresenting and retrieving documents. 2.25

c04Identi! the essential components and functions of an information

retrieval system.2.25

c05,{pply IR principles to locate relevant information large collections

ofdata. 2.2s

Faculty Signature

IRS

Name of Faculty: Dr.Mohanty

Branch & Section: lT

Subject: IRS

Course outcome attainment

CO-PO ma

Faculty


Department of lnformation Technology

Program Outcome AttainmentAcademic Year: 2015-16Year: lV

OVERALT

Semester: I

co Mid-1 Mid-2 AVG Univ DIRE( INDIREI

co1 3 2 2.25 1.8

3co2 2 2.25 1.8

co3 2 2.25 1.8

3 2 2.25 1.8co4

co5 3 2 2.25 1.8

ATTAINMENT ##di## 2 2.25 #Drv/o! 1.80

IIII IIIIIIPO1 P02 PO3 PO4 PO5 PO6 P07 PO8 PO9 PO10 PO11 POt?

2 2co1

2 1co2

3 I 2co3

2 Ico4

2 2cos

co63 2 2 2AVERAGE

ATTAINMENT

IIIIIIIIIIIIIIIII IIIIIIIIIIIIIIIIIIIIIIIIIIIII IIIIIIIIIIIIIIIIIII

IIII III III

II

KESHAV MEMORIAT INSTITUTE OF TECHNOLOGY

Department of lnformation Technology

Program Specifi c Outcome AttainmentName of Faculty: Dr.Mohanty Academic Year: 2015-16

Branch & Section: lT Year: lV

Subject: IRS

Course outcome attainment

CO-PSO mapping

Faculty

llndMid Univ OVERALLco lstMidco1 3 2 2.25 1.8

co2 3 2 1.8

3 2 1.8co3co4 3 2 2.25 1.8

co5 3 2 2.25 1.8

average 2.25 #Drv/o! 1.80

IIIIIIIIII

PSO2

1co1

co2

2 1co3

2co4

1 2co5

averaSe 2 2

attainment

I

II

DTRECT lrrUOrnrCr

2.2s1

2.2s1

PSOlI

KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY

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