The cultural classification of ‘things’: A system network for English noun senses

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The cultural classification of ‘things’Towards a comprehensive system network for English noun senses

Robin P. FawcettCardiff University, United Kingdom

We begin by distinguishing between an ontology and a semantic system net-work for noun senses. i.e. a cultural classification of ‘things.’ Next, we locate this in a systemic functional lexicogrammar, and evaluate two ways of represent-ing it. Then we explore: (i) the reason for its overall structure; (ii) the central role of probabilities; (iii) how to model the [mass] v [count] distinction (and two others); (iv) two crucial non-taxonomic relationships; (v) three types of non- experiential meaning; and (vi) the key role of realization rules in generating nouns as heads of nominal groups. With nearly 5,000 noun senses, the Cardiff Grammar’s network for the cultural classification of ‘things’ is probably the largest in existence. Yet it needs further development, and readers are invited both to use this valuable resource and to consider helping to expand it.

Keywords: ontology, semantic system network, noun senses, cultural classification of ‘things,’ modelling lexis, probabilities in lexis

1. Introduction

This paper is set within the general framework of Systemic Functional Linguistics (SFL) and, in particular, in the explicitly “cognitive-interactive” version of SFL that has been developed in the period since the mid-1970s by my colleagues and myself, largly at Cardiff. It is, however, a version in which Halliday’s concern for the “social” and the “cultural” also has an important place. The component for language itself has become known as “the Cardiff Grammar” – particularly since its very full description in Butler (2003a, 2003b). The term “the Cardiff Model” is used to refer to the full architecture of language and its use, within which the Cardiff Grammar is the core component. Relevant major works include Fawcett


54 Robin P. Fawcett

(1980), Tucker (1997), Fawcett (2000/10), Huang (2003), Fawcett (2008), and Fawcett forthcoming (2015a), to mention only selected book publications.1

However, the central aspect of language that is our topic here is one that is a challenge to all theories of language, so that this paper should interest all lin-guists – including formal linguists.

The original stimulus for developing the system network that this paper describes was Halliday’s challenging proposal, back in 1961, that “the grammar-ian’s dream is […] to show that lexis can be defined as ‘most delicate grammar’ .” Surprisingly, perhaps, the version of SFL in which this ‘dream’ has been made a reality is not the Sydney Grammar, as one might expect, but the Cardiff Grammar. Moreover, this “reality” consists not only of a number of full and explicit publica-tions (for which see Section 4) but also a description of large portions of English that is sufficiently explicit to be incorporated in a computer implementation of the Cardiff Grammar as a generator of text-sentences.

The work described here was carried out in the framework of the COMMUNAL Project at Cardiff University, of which I was the Director. While

1. The model of lexis presented here was developed by Gordon Tucker and myself, as we explored the implementation of an idea suggested by Michael Halliday. It is a core component of the Cardiff Grammar, and so of the COMMUNAL Project in the Computational Linguistics Unit at Cardiff University from 1987 to 2000, of which I was the Director. This project was supported for over ten years by grants from the Speech Research Unit at DRA Malvern (later privatized as QinetiQ) and Cardiff University (and by ICL and Longman in Phase 1). I thank all these institutions for supporting this ‘blue sky’ research in a period in which most spon-soring bodies placed a disturbingly strong emphasis on short-term research with potentially marketable outputs. But I also want to express my thanks to the many friends and colleagues who have supported my work over the years. The two scholars and friends to whom I am most indebted are: (1) Michael Halliday, the ‘father’ of Systemic Functional Linguistics and the lin-guist to whom I, like many others, owe the basic concepts of my current model of language, and (2) Gordon Tucker, who has worked closely with me for many years on (i) developing the version of Systemic Functional Grammar (SFG) that has come to be known as the Cardiff Grammar, and (ii) in implementing it in the COMMUNAL computer model of language and its use. Many of the ideas presented here are the results of our joint work, so that any credit that may be due for the work described here should be shared (as often in fruitful research). And for many hours of work with me on the later stages of expanding the original system network for noun senses to its present size, we owe particular thanks to Dr Fiona Barker (née Ball). Finally, I must mention the debt that my colleagues and I have to Chris Butler. His approving reviews of our publications and his insightful descriptions of the Cardiff Grammar in all the relevant sections of Butler (2003a) and (2003b) have been a great source of encourgement to us, in a period when many of our fellow systemicists have failed to see the value in our work as an exten-sion of Halliday’s proposals. Butler’s conclusion that “the Cardiff model represents a substantial improvement on the Sydney account” (Butler, 2003b, p. 471) was particularly pleasing, coming as it does from the most widely respected evaluator of functional theories of our time.


The cultural classification of ‘things’ 55

this was primarily a project in the field of Natural Language Generation (a branch of Computational Linguistics), our main motivation was to use the challenge of building a natural language generator to push us to develop a fully explicit and updated systemic functional model of language and its use. The result was (i) the version of Systemic Functional Grammar that has come to be known as “the Cardiff Grammar” and (ii) the overall architecture of language and its use that is known as “the Cardiff Model.” Here we shall focus on the linguistic aspects of that body of work rather than on the computer implementation.

In Tucker (1996), my colleague Gordon Tucker describes the same component of the Cardiff Grammar that this paper describes, but at an earlier stage in its development and in less detail than I do here. At that stage we were still evaluating some of the concepts that are identified here as central to the model, and the pres-ent work may be regarded as the definitive description of this major component of the Cardiff Grammar.

This paper, then, provides the fullest description yet published of (i) the system network for the cultural classification of ‘things’ (i.e. the network for “noun senses”), (ii) its relationship to the equivalent ontology in the belief sys-tem (briefly), (iii) its functions in the overall grammar, (iv) its size and scope, (v) the reasons for its internal structure, (vi) the various types of feature that it contains, (vii) the realization rules through which it generates nouns and finally (viii) instructions (in Section 4) for how to obtain a copy of the current version of the full network – together with an invitation.

2. The place of the network in the overall model of language

2.1 The components of a model of language

Let us begin by establishing the level of language at which the system networks function in the two versions of SFL: the Sydney Grammar and the Cardiff Grammar.

Many of Halliday’s writings in the 1970s and 1980s show that he was explor-ing alternative approaches to modelling “meaning” in that period. Indeed, he has described the system networks for transitivity, mood etc., in at least these three ways:

1. as consisting of “semantic options” that “represent the ‘meaning potential’ of language” (Halliday, 1970, pp. 142, 144), with a slightly revised version in Halliday (1970/2002, pp. 174–175),


56 Robin P. Fawcett

2. as having been “pushed […] fairly far […] in the direction of the semantics” (Halliday, 1994, p. xix), and

3. as being at the level of “wording” rather than “meaning,” so at the level of form – and so essentially as the system networks were in his earlier Scale and Category model (Halliday, 1961), and as he now appears to assume again, e.g. in Halliday and Matthiessen (1999) and Halliday and Matthiessen (2004).

Yet these apparently far-reaching changes in the theoretical status of Halliday’s sys-tem networks have not been matched by equivalent changes in his actual description of English. This has remained largely unchanged since the 1970s. And this sug-gests that, in practice, Halliday’s system networks are at (or are close to) the level of meaning – and so are at essentially the same level of language as those of the Cardiff Grammar. So the very large system network described here (for which the Sydney Grammar has no equivalent) could be adopted by those using the Sydney Grammar.

Figure 1 presents a model of how a systemic functional model of language works. This holds for both versions of SFL; compare Figure 1 with its equivalent in Matthiessen and Bateman (1991, p. 102). It has two main components (on the left), and two outputs (on the right), one from each component. This “grammar” consists of two “potentials”: the system network of semantic features at the level of meaning (which specify the meaning potential of the language) and the realization rules at the level of form, which specify the form potential (i.e. the structures etc. that can be built). More accurately, it is a lexicogrammar (to use Halliday’s term) since it generates lexis (vocabulary) as well as grammar (syntax, morphology and grammatical items) – and also intonation and punctuation.

potential instance

meaning system networkof semantic features

selection expressionof semantic features

formrealization rules one layer of a richly

labelled tree structure

Figure 1. The main components of a Systemic Functional Grammar and their outputs.

When the grammar is in use it generates two types of instance. Each traversal of the system network results in a selection expression of the semantic features chosen on that traversal. The realization rules then take these as their input and state the ways in which the semantic features will be realized at the level of form. So they specify the “form potential.” An output from the realization rules consists of a



syntactic unit (e.g. a clause) its elements, and the items that expound them – plus its intonation or punctuation.

However, one or more of that unit’s elements often needs to be filled by a fur-ther unit (e.g. a nominal group), and the arrow on the left represents the type of realization rule that specifies a re-entry to the network to generate another unit. Thus each traversal of the network chooses the features relevant to one semantic unit, and each generates one syntactic unit – plus its intonation or punctuation.

Essentially, then, a lexicogrammar is the sentence-generating component of a full model of language and its use, and the system network described in this paper is the sub-network from which the nouns in a text-sentence are generated. More specifically, it is a sub-network of the network for ‘thing’ (from which all nominal groups are generated). Normally this is not encountered till the second (or a later) pass through the network, when the purpose is to generate a nominal group that will fill an element of a clause. (See Chapter 2 of Fawcett, 2008 for a fuller descrip-tion of this model, and Fawcett, Tucker & Lin, 1993 for a complete description of how a sentence is generated in SFL.)

There is one major difference between the two versions of SFL that is highly relevant to this paper. The theoretical position taken by Halliday and Matthiessen (e.g. 1999, pp. 198–201) on the question of how to model lexis in SFL is essen-tially the same as that taken here. But no linguist working in the framework of the Sydney Grammar has gone beyond exploring fragments of system networks for very limited areas of English lexis (one much-cited early example being that by Hasan (1987/96). Disappointingly, the computer implementation of Halliday’s model in the Penman Project uses a ‘standard lexicon’ approach (because that was what the sponsors of the research required). In contrast, we who work in the framework of the Cardiff Grammar have developed very large networks for generating all of the major classes of lexical item, and so given Halliday’s chal-lenging theoretical proposal the fullest possible test. It is good to be able to report that we have found it to be an insight of extraordinary value (while needing one major modification, as I shall explain in Section 3.4.3). We have written system networks and their realization rules for generating nouns, lexical verbs, adjectives and manner adverbs, and we have tested them in the computer implementation of the Cardiff Grammar in the COMMUNAL Project.

Thus one of the strengths of the Cardiff Grammar is that it is not just a gram-mar (in the sense of a model of syntax and morphology); it is a genuine lexico-grammar – and so has precisely the components that Halliday has always proposed that systemic functional models of language should have. See Chapter 2 of Tucker (1997) for an excellent history of “Approaches to lexis in systemic linguistics,” and see Section 4 of this paper for notes on where to find Cardiff Grammar descrip-tions of system networks for all areas of lexis.


58 Robin P. Fawcett

2.2 Above language: The relationship between a system network for noun senses and an ontology of objects

Our work over two decades in the COMMUNAL Project on the Cardiff Model of language and its use has taught us that one of the most important lessons to learn, when trying to model language and its use, is:

do not try to do too much work in any one component.

Thus the key to modelling language successfully is to have a sufficiently holistic theory – and so a sufficiently complete model – and to be able to identify the appropriate component – often referred to as a “level” – at which each particular type of work should be done.

We shall begin, then, by recognizing that an adequate model of language and its use must include both (i) an ontology of concepts in the belief system and (ii) a broadly corresponding system network of noun senses in the language itself, through which these concepts can be expressed.2 For successful communication to occur, there must be relatively similar (though inevitably non-identical) ver-sions of each of these components in the minds of both the performer and the understander of a text.

Let us focus on the production – or generation – of a text. Once you extend your model of language and its use to include the unconscious planning – and so reasoning – which inevitably precedes the production of spoken or written text, you commit yourself to having a number of components that lie outside language. (These are therefore clearly not “levels of language.”) One such component must be able to serve the type of reasoning that refers to what is often termed “property inheritance,” and this component is the ontology in the belief system.

However, while the system network for noun senses that lies inside the lan-guage system influences the equivalent part of the ontology, the two are not identi-cal. This is because the functions that the system network for noun senses serves are different from the functions that an ontology serves. The main function of the ontology is to subclassify classes of object and so to facilitate reasoning. So what are the functions of the cultural classification network in the semantics of a language? The next section will answer this question.

2. For a discussion of the nature of ontologies, see Fawcett (1994), and for a description of the ontology in the Sydney Model see Bateman (1990).



3. The functions and structure of a system network for noun senses

3.1 The two major functions of the system network

The first of the two major functions of the system network that we shall consider here is self-evident. Just as the function of the system networks for transitivity, mood, theme, etc. is to generate clauses and their elements, so it is the function of the network for the cultural classification of ‘things’ to generate nouns that expounds the head of a nominal group.

Before going further, we should note a couple of key SFL conventions. One is that the names of system networks are written in capitals (as above) and the other is that the features in the networks (to which we shall come shortly) are written in lower case – typically enclosed in square brackets but sometimes, in informal use, in single quotation marks. And, when a feature consists of more than one word, its parts are sometimes converted into one orthographic unit by replacing the spaces with underscores. (This practice originated in the requirements of the computer program through which the grammar is implemented, and it is used occasionally in this paper).

The second major function of the network is to model the various ways in which the meanings of nouns are related to each other. This is the representa-tion in SFL of the traditional semantic relations between words, as described, for example, in Chapter 9 of Lyons (1977). The types of relationship between noun senses that we are considering here are therefore paradigmatic relations, not syn-tagmatic relations.3

It is to these relationships between noun senses that the name of the system network – cultural classification – refers. Clearly, two concepts contribute to its name: “culture” and “classification.”

Let us take “classification” first. We shall examine the principles that determine the network’s internal structure in Section 3.2.3, so here we shall simply note that its overall structure is (like that of an ontology) a simple taxonomy – i.e. a classification of types of ‘thing,’ with the potential depth of subcategories often being over ten. Thus an ‘oak’ is a subcategory of ‘tree,’ and ‘tree’ is a subcategory of ‘plant,’ and so

3. Syntagmatic relations require a completely different approach. In the Cardiff Grammar we model these through the various structures by which items, including nouns, are related to each other, and in a later section we shall touch on the most important way in which this is done (via nouns at the heads of nominal groups that fill the Participant Roles associated with specific types of Process). This approach is more insightful, in our view, than the approach through the con-cept of collocation (spans, collocates, etc., e.g. as described in Sinclair 1991) – valuable though this has been as an interim stage in exploring lexical relations in the syntagmatic dimension.


60 Robin P. Fawcett

on. In the terms used in SFL, this system network is based on experiential meaning. But there are complications to this simple picture, as we shall see in later sections.

In what sense, you may ask, is the classification of ‘things’ “cultural”? The answer is that each social group, large or small, has its own culture (i.e. beliefs of all kinds, including an ontology of objects) and its own language. Each cultural classification differs to some extent from any other. So the cultural classification of ‘things’ in Chinese differs to some extent from the equivalent classification in English, or any other language. Similarly, the dialect of one sub-group differs slightly from that of another, and the idiolect of one individual differs slightly from that of any other. Nonetheless, most adult speakers of a language share a large pro-portion of their cultural classification of ‘things,’ and without this communication through language would be impossible.

The next question is: Whose cultural classification should the linguist try to model? In the COMMUNAL Project the answer was that it should reflect the unthinking assumptions that a reasonably educated native speaker of British English is likely to have absorbed as she or he learned and used the language. So it is not the beliefs of an “expert,” and therefore not a scientific taxonomy. Instead, the system network that you are about to meet is unashamedly anthro-pocentric (and occasionally britocentric). It includes unscientific categories such as ‘ creeping_thing’ and ‘fish_like_mammal.’ Thus it will not class a ‘dolphin’ as a subcategory of ‘cetacean’ but of ‘fish_like_mammal.’ (This brings with it the inci-dental benefit of enabling this class of ‘thing’ to enter the same sub-network for its ‘parts’ as does ‘fish.’ For the ‘part_of_x’ relationship, see Section 3.5).

To summarize so far: the system network for the cultural classification of ‘things’ is a folk taxonomy rather than a scientific taxonomy; it shapes the culture of the society that uses it; and its two major functions are (i) to provide the means for generating nouns that expound the heads of nominal groups and (ii) to repre-sent the relations between the noun senses of English that are the “common core” of what is available to native speakers of English.

The network also serves a number of very specific functions, and these will be introduced over the next few sections, as we consider why the structure of the network has the various characteristics that it has.

3.2 The structure and scope of the system network

3.2.1 The overall structure: Two ways of representing the system networkThe questions to be addressed here are these: What is the overall structure of this system network? and: How should it be represented in two-dimensional space, as here? The answers given here are derived from the experience of developing a



network of almost 5,000 noun senses for English, including very full sub-networks for many large areas of meaning (as described in the next sub-section). We shall illustrate the network as a whole by describing the early choices in the system network, since a discussion of these raises most of the main issue that contribute to its overall structure.

Since it is a system network of meanings, one of the key requirements is that it must be able to model the fact that a single form is frequently the realization of several different meanings. Such cases of polysemy are, of course, just one of many problematical characteristics of natural human languages. One of the practical problems in building this network is that of finding appropriate labels for the two (or more) semantic features that are realized in a single noun (e.g. “head” as part of a creature, part of a valley, as an informal way of denoting a head teacher etc.). Surprisingly, perhaps, in this network we allow ourselves to use the same term for the semantic feature as the word form itself (but with a suffix, as we shall see shortly). It would be misleading to claim that by labelling the meaning of “water” as ‘H20’ we had somehow captured its meaning more insightfully than by simply terming it “water.” English is full of examples of polysemous forms that need to be differentiated in the semantic system network, and we use appropriate suffixes to distinguish between them.

The best way to introduce you to the network is to present a slightly simplified version of the initial substantive systems. (For the meaning of “substantive” see Section 3.5). And this brings us to the question of how best to represent a system network. Indeed, one of the most important points to grasp about a system net-work is that it is not a form of representation but a concept.

There are two ways of representing system networks that are in regular use. The first – and the one that you will encounter most frequently in the literature of SFL – is the graph representation that is used in Figures 2 and 3. The second is the linear (rule-based) type that we shall meet in Figure 4. We shall consider the graph version first, since it was Halliday’s original form of representation and it is the form found in most introductory textbooks. Please look now at Figure 2.

The first system in Figure 2 shows a system which presents a choice between three semantic features: [physical_thing] v [abstract_thing] v [event_thing], with the entry condition to the system being [thing]. (Reminder: features from networks are by convention shown in square brackets, when they occur in running text). Each of these three features is in turn the entry condition to a further system – and so on, building up a vast network of semantic features. As you can see, this network extends to a depth of seven features in one part of Figure 2 and, since [mammal] is the entry condition to the sub-network in Figure 3, the network may have further features before reaching a terminal feature (not shown here, but indicated by the examples).


62 Robin P. Fawcett

whole_plant

grass / fungus / low_form_of_plant)plant

living_thing

fruit / etc.)

whole_creature mammal (to Figure 3)

bird (tame / wild)creature

_by_formreptile

part_of_creature (mind /

amphibiancreeping_thing

physical_thing

physical_part) bugsub_bug

for_communicationuse_of_landbuilding

artefact vehicle_for_transportclothing

thing_as_object for_human_consumptionfurniture / etc.

non_living_thing

natural_thing_as_object

skything celestial_body

earth_surfacegeneral_physical_phenomenon

energy

colourweather

thing_as_substance

attribute_of_thingstate_of_thingabstract_quality

abstract_thing abstract_quantityidentity_relationspositiontime / etc. material_event

mental_event (emotion / perception /event _as_process relational_event cognition)

environmental_event

event_thing accidentexperienceceremony

complex_event recreationaloccupational

eatingtravelling / etc.

Figure 2. A simplified graph representation of some of the initial substantive features in the system network for noun senses.



_stage_and_genderadult (man / woman /lady / etc.)

childadolescent

(boy / girl)

whole_human

family_role (mother / son / etc.)interpersonal_role (friend /etc.)role_by_behaviour (fool / etc.)work_role /etc (teacher / etc.)

human(American / etc.)

part_of_human (head / chest / back / limb / etc.)

mammal group_of_humans (occupational / socio_political / recreational /educational / etc.)

as_near_relation (ape / monkey)

non_human

typically_as_pet (cat / dog / hamster / etc.)typically_tame for_riding (horse / pony / donkey / mule)

farmed_mammal (bovine / pig / sheep / etc.)

british (rabbit / squirrel / mouse / fox / etc.)typically_wild northern (bear / wolf / seal / etc.)

etc.)

etc.)

Figure 3. A simplified graph representation of some of the more delicate substantive features in the system network for noun senses.

Now please examine Figure 4, which illustrates the second way to represent a sys-tem network. Then we will be in a position to compare the two types.

As you will have guessed, each of the two forms of representation has its advantages and its disadvantages.

One advantage of the graph representation is that it is conceptually more eco-nomical – and so more elegant – than the linear representation in Figure 4. The reason is that it avoids the repetition of features that are required in the linear representation, in which every feature (other than the initial and terminal ones) appears twice – both as a choice in a system and then again as the entry condition to a dependent system.

A second advantage of the graph representation to a human reader of the diagram is that, if the network is relatively simple in its overall structure (as the one we are considering here is), the reader’s eye can follow the pathway of features through the network to the terminal feature – and so often to the realization at the level of form of this selection expression of features. This is helpful when a system network is being used for analyzing a text at the level of its ‘strands of meaning’ (e.g. as illustrated in Fawcett (2000/2010, p. 148) and Fawcett (2008, p. 242).


64 Robin P. Fawcett

thing … [several other features] … → CULTURAL_CLASSIFICATION.CULTURAL_CLASSIFICATION → physical_thing / abstract_thing / event_thing.physical_thing → living_thing / non_ living_thing.living_thing → plant / creature.plant → whole_plant / part_of_plant.whole_plant → tree / bush / creeper / �eld_crop / �owering_plant / non_�owering_plant / grass / fungus / low_form_of_plant.part_of_plant → root / stem / branch / leaf / fruit / etc.creature → whole_creature / part_of_creature.whole_creature → speci�ed_by_role / speci�ed_by_form.speci�ed_by_role → domestic / wild.speci�ed_by_form → mammal / bird / �sh / �sh_like_mammal / reptile / amphibian / creeping_thing / bug / sub_bug.mammal → whole_mammal / part_of_mammal.whole_mammal → human / non_human.human → whole_human / part_of_human / group_of_humans.whole_human → speci�ed_by_stage_and_gender / speci�ed_by_role / speci�ed_by_nationality / etc.speci�ed_by_stage_and_gender → adult / adolescent / child.speci�ed_by_role → family_role / interpersonal_role / role_by_behaviour / work_role / etc.non_human → as_near_relation / typically_tame / typically_wild.typically_tame → typically_as_pet / for_riding / farmed.typically_wild → british / northern / tropical.part_of_creature → mind / physical_part.non_living_thing → thing_as_object / general _physical_phenomenon / thing_as_substance.thing_as_object → artefact / natural_thing_as_object.artefact → for_communication / use_of_land / building / vehicle_for_transport / clothing / for_human_consumption / furniture / etc.natural_thing_as_object → sky / celestial_object / earth_surface.general_physical_phenomenon → energy / weather / colour.abstract_thing → attribute_of_thing / state_of_thing /abstract_quality / abstract_quantity / identity_relations / position /time / etc.event_thing → event _as _process / complex_event.event_as_process → material_event / mental_event / relational_event / environmental_event / in�uential_event.mental_event → emotion_event/ perception_event / cognition_event.complex_event → accident / experience / ceremony / recreational / occupational / con�ictual / eating /travelling / etc.

Figure 4. A simplified linear, rule-based representation of the initial substantive features in the system network for noun senses.



But the graph representation also has some serious disadvantages. First, it takes a considerable amount of time to draw such a diagram neatly, and even more time if you decide to modify it. Second, you cannot fit a network with a depth (or delicacy, to use Halliday’s elegant term) of more than a few systems onto a single page – as Figures 2 and 3 demonstrate. Indeed, graph diagrams always takes up more space than linear diagrams (e.g. Figure 4 occupies only two thirds of the space of Figures 2 and 3, while including slightly more information).

But the graph notation often has a third and more serious disadvantage (although it is not in fact a problem in the present network). It is one that nor-mally only becomes crucial in research projects that involve building system net-works with something approaching the full complexity of the grammar (rather than being merely an introductory presentation). The problem is the complexity in the “wiring” for the entry conditions to many of the systems. In many areas of the grammar it becomes such a jumble of “and” and “or” relations that the graph representation becomes virtually impossible to draw – let alone to interpret. In contrast, the linear, rule-based version – especially when combined with indenta-tion to indicate the relative depth of the systems (when space allows) – can handle all the complexity that is required and still remain readable by a human (e.g. one who is checking the grammar) – as well as functioning as part of a computer model of language generation.

To summarize: the graph representation may be helpful for introductory work in which a simplified version of the lexicogrammar is being presented, but the lin-ear, rule-based representation is essential for building a fully explicit and reason-ably comprehensive generative systemic functional grammar. And it is far quicker to write and to correct.

From this point on, you are invited to consult whichever version of the network you prefer, when considering the questions to be addressed in the rest of this paper.4

However, there are two vital aspects of a complete system network that are missing from Figures 2, 3 and 4, and we must now consider these. Consider the full representation of the initial system of the network in Figure 4 given here:

CULTURAL_CLASSIFICATION → 99.1% physical_thing / 0.2% abstract_thing / 0.6% event_thing / 0.1% phenomenon_c (66.001).

4. Since these are all “substantive” features that express the ‘type of ’ relationship, it may be of interest to point out that these semantic features have equivalents in the ontology in the belief system of the Cardiff Model of language and its use. The consequence is that you could, if you wished, use this summary to compare the categorization of the universe of objects that is made in the Cardiff Model’s ontology with the categorizations presented in other ontologies.


66 Robin P. Fawcett

The first omission in Figures 2, 3 and 4 is the percentage that precedes each feature in a system. This expresses the general probability that this feature will be chosen. Interestingly, the grammar provides for such probabilities to be changed during the process of generation, and we shall see the benefits that this brings to the gram-mar in Section 3.3.

The second omission is is the number of the realization rule that is attached to each terminal feature in the network. To illustrate this I have included a fea-ture omitted from the simplified initial system in Figures 2 and 4, namely [phenomenon_c]. As its probability of 0.1% shows, it is an infrequently selected choice. But it useful because it illustrates the concept of a realization rule. (Note the final “_c.” This indicates that English treats a ‘phenomenon’ as something that is countable, and that its realization will be as a “count noun.” Section 3.4.3 will explain the vital role played by this and similar suffixes on terminal features.

The realization rule for [phenomenon_c] states:

66.001 : phenomenon_c : “irrn”(phenomenon_c).

But this is an unusual rule, because the word phenomenon has an irregular plural. So let us first consider a typical rule such as:

72.93 : dolphin_c : h < “dolphin.”

This rule states: If [dolphin_c] is selected, the head (h) of the nominal group that the grammar is currently generating will be expounded by (<) the item “dolphin,” and the plural of “dolphin” will be “dolphins.” (This rule is taken directly from the computer version of the grammar, in which double quotation marks rather than italics (the usual convention in Linguistics) are used to indicate an item (a word or morpheme) in running text).

The rule for [phenomenon_c] is different because phenomenon is an irregular noun (signalled by “irrn”). This directs the user of the grammar (whether a human or a computer program) to the table of irregular nouns, and within it to the entry for [phenomenon_c]. This states that, if [singular_cc] has also been chosen, the head of the nominal group will be expounded by the item phenomenon but that, if [plural_cc] has been chosen, it will be phenomena. (The suffix “_cc” stands for “cultural classification,” and this distinguishes it from the type of ‘plural’ realized in “they,” which is generated from a different realization rule. I have given the full details of these examples, in order to illustrate the attention to detail that is given in the Cardiff Grammar, and the commitment to modelling explicitly the proce-dure by which a meaning is realized as a form.)

Interestingly, the term “noun” plays no part in the formalized version of the grammar. Its status is simply that it is a useful term for referring to the set of



items that (i) expound the head of a nominal group and (ii) denote the cultural classification of ‘things.’

We have now seen (i) examples of two alternative representations of some of the early systems in this system network, and (ii) how the associated realization rules operate.

3.2.2 The size and semantic scope of the system networkA few minutes spent examining either Figures 2 and 3 or Figure 4 will give you a good sense of the overall semantic scope of the network. For example, the fact that it includes ‘event things’ is important, because this shows that the network provides not only for ‘physical things’ and ‘abstract things’ but also for ‘things’ that are a representation of an ‘event.’

To get an idea of the scope of the network in quantitative terms, let us consider first the sub-network of the types of [physical thing] that a [non-living thing] may be – and then, as a sub-type of that, the sub-network for a [thing_as_object]. As the network shows, one sub-type of [thing_as_object] is [artefact], and there are currently fourteen sub-networks for types of artefact. Notice that we are already at a depth of five subcategorizations. As a general indication of the further depth of the coverage of the system network, consider the fact that one of these sub-networks – the one for things [for_human_consumption] (i.e. food and drink) – contains 137 systems and generates 330 different noun senses. Another area is [use_of_land], and this includes the following sub-categories: [built_up_area], [countryside], [for_travelling], [for_recreation], [wasteland] and [for_dividing_land]. So far, [use_of_land] has 135 sub-systems, generating 370 noun senses. A third relatively well-developed area is that of plants, where there are 130 systems that together generate 246 noun senses. (This, as was explained in Section 3.1, reflects a layman’s taxonomy of different trees and flowers, and a botanist or a keen gardener could well have several hundred more).

The reason for citing these examples is that they happen to be ones that have been systematically developed to a point that we think roughly reflects the usage of a non-specialist native speaker of English. There are quite a few other areas of the overall network which are similar in size but which need further development.

To summarize: the system network for the cultural classification of ‘things’ is already very large, totalling at the last count just under 5,000 noun senses, and is available for further development. See Section 4.

Notice that the figure of 5,000 covers only the meanings of nouns; see Section 4 for an estimate of the total number of word senses covered in the Cardiff Grammar, and so for its total vocabulary.


68 Robin P. Fawcett

3.2.3 The principles underlying the system network What, then, are the principles on which the system network is organized? The simplest network for the noun senses of English would consist of just one system, containing a very long list of noun senses, each realized by a noun form – i.e. a single, massive system with 5,000 or more features in it. Why do we not adopt this simple model?

The main linguistic evidence is our awareness, as users of the language, of two predominant relations between nouns – i.e. between noun senses. These are the rela-tionships of hyponymy and contrast. Thus the word-form “dog” is a hyponym of the word-form “mammal,” and its meaning – represented as [dog] – is a subordinate cate-gory of [mammal]. So the semantic feature [dog] is systemically dependent (via three systems) on the feature [mammal]. And the word-form “dog” is in contrast with the word-form “cat,” because the meaning [dog] is in systemic contrast with the mean-ing [cat]. Standard works on lexical semantics, such as the relevant portions of Lyons (1977), recognize the centrality of these two concepts (while also introducing others). And these two relationships are precisely those that system networks are designed to model – although, interestingly, Halliday first introduced system networks as a way to model grammatical rather than lexical relations; see Halliday (1961).

There is concrete evidence that such contrastive and hyponymic relations between noun senses are an inherent part of the language – and that these relation-ships are therefore required in the semantic system network within language as well as in the ontology in the “higher” component of the belief system. The evidence is the existence of large numbers of nouns – and so noun senses – that denote super-ordinate categories, such as “animal,” “mammal,” “human,” etc., including “thing” itself. (These, as we shall see in Section 3.5, are expressed in the system network as what we term “as such” features).

The overall structure of the system network for the cultural classification of noun senses is therefore taxonomic, and the main way in which individual systems are related to each other in a network is represented in the graph notation as in Figure 5 (as used in Figures 2 and 3).

a

bd

e

f

g

h

c

Figure 5. The graph notation for a simple system network.



Figure 5 can be read as “If ‘a’ then ‘b’ or ‘c.’ ” But in SFL f it is typically expressed as: “If ‘a’ is chosen (by a speaker or writer or the “decider” in a computer program) then either ‘b’ or ‘c’ must be chosen.” As Figure 5 shows, a feature may also be the entry condition to a dependent system, so building up a potentially vast system network (as in Figures 2 and 3).

Within this general framework, we shall now note an important general guideline in constructing networks for lexis.

In general, systemic linguists like to incorporate the concept of simultaneity into their system networks. This occurs when two or more systems are entered in parallel from a single entry condition (whether simple or compound). This is represented in a graph diagram as a right-opening curly bracket, signifying ‘and.’ The reason for its popularity is that such networks appear to be more elegant, in that they appear to capture relatively more combinations of features with fewer systems.

Here is a simplified example of this formalism in use – this time, using the more economical linear representation:

fruit_vegetable_plant → tomato_plant_c / pepper_plant_c / courgette_plant_c & plantness_explicit / plantness_implicit.

The effect of choosing [plantness_explicit] is that the grammar generates a com-pound noun such as “tomato plant,” whereas if [plantness_implicit] is chosen it simply generates “tomato.” So this little bit of lexicogrammar would generate the following six nouns for denoting a tomato plant: “tomato,” “pepper,” “courgette,” “tomato plant,” “pepper plant” and “courgette plant” (i.e. three simple nouns and three compound nouns).

Surprisingly, perhaps, we do not model these data in this apparently attrac-tively economical way in the Cardiff Grammar. Years of experience in working on these problems in the COMMUNAL Project have taught us that, in general, it is advisable to avoid simultaneous system networks for lexis (and also, inciden-tally, to use them with great care when modelling syntactically realized options). One reason is that there are in fact relatively few places in the overall network where (i) the same features and (ii) the same probabilities apply. We can model the data more sensitively if we capture any such generalizations that are valid by the alternative systemic relationship that is illustrated in Figure 6 (to which we shall come shortly).


70 Robin P. Fawcett

But there is a second and more serious problem with simultaneously entered systems. It is that they may accidentally permit unintended combinations of fea-tures for which there is no realization. As an example, consider the variation in the subcategories of animals on the dimensions of ‘stage of life’ and ‘gender’ that are available in English. These vary greatly from one type of animal to another, and we therefore need to create different sub-networks for most species of animal. It would be a serious mistake to allow even a simple system of [male] v [female] to be entered in parallel with the system that leads to all of the different types of ‘crea-ture,’ because most users of English only have words that express the distinction by gender for a very limited number of types of creature (typically farm animals), such as “bull” and “cow,” and “ram” and “ewe.” But we don’t have words to denote male and female giraffes, snakes or insects, so it would be wrong for the system network to imply that we do.5

In the Cardiff Grammar, therefore, we permit the frequent repetition of sys-tems that contrast ‘gender’ or ‘stage of life,’ each with its appropriate probabilities, so making the overall network look, superficially, less elegant than it might be. But this in fact makes it more rather than less elegant, because it more accurately reflects the taxonomy in our minds. In modelling language to a high standard, accuracy trumps superficial elegance. Thus the approach described here allows us to have slightly (or greatly) different sub-networks for each type of ‘creature’ – and so greater descriptive accuracy.

Anticipating Section 3.3, we can say the essentially the same principle applies when the difference between the sub-networks for two species lies not in the dif-ference between the features themselves but between their probabilities. Contrast, for example, the low general probability of using “doe” and “buck” to differentiate adult rabbits with the high probability of using “cow” and “bull” to differentiate adult cattle.

5. Unfortunately, networks in which the over-use of simultaneously entered systems that imply the existence of nouns that don’t exist can be found occasionally in the SFL literature. One clear case occurs in Berry’s early introductory textbook (Berry, 1977, p. 62), and there is another in Cross (1993). And there is an apparently similar case in the “feature network” for clothing used in Halliday & Matthiessen (1999, pp. 198–201). We should note, however, that this is not pro-posed as a system network at the intra-linguistic level of meaning that we are considering here, but at a putative higher level of semantics. At one point Halliday & Matthiessen suggest that this “semantic” level of representation may be broadly analogous to the Penman Upper Model (Bateman, 1990), in which case this “feature network” would in fact be an ontology. And, since the features in ontologies do not have realization rules attached to them that specify how they are to be realized at the level of form as a noun, it could well be argued that these “lexical gaps” are not a problem. There is much that remains unclear about Halliday & Mathiessen’s claims for this “higher” level of paradigmatic relations, as Butler points out (2003b, p. 470).



However, when the probabilities in the possible subordinate sub-network do not vary with the species, an alternative and more economical solution is possible. This is to gather the relevant features together into a disjunctive entry condition, as in Figure 6, where both ‘e’ and ‘f ’ lead to the system of ‘g’ v ‘h.’

a

bd

e g

hf

g

h

c

Figure 6. A system with a disjunctive entry condition.

The key point, then, is that a network for noun senses must not allow the user to choose a set of features for which there is no realization as a noun. So here too the present description illustrates the level of care with which the network has been developed.

3.3 The central roles of probabilities in the system network

One of the most important characteristics of the overall system network in the Cardiff Grammar is that probabilities are used throughout it. We have already noted their function in modelling the general probabilities that are an inher-ent aspect of the description of this area of the language, but they have a second major role. The lexicogrammar also provides that these general probabilities can be changed, under specified circumstances. This procedure is termed preselection, and the type of rule that is used is a preference re-setting rule.

Such rules typically occur when the realization rule for a feature that has been chosen when generating a clause specifies changes to the probabilities when the network is re-entered, e.g. to generate the head of a nominal group that fills one of its elements. And, since the nominal group is quite likely to include a noun as its head, preference re-setting rules often change the probabilities in the network for the cultural classification of ‘things.’

Let us look at how this effect is achieved. Process types (which are typically realized as lexical verbs) have configurations of Participant Roles (PRs) associ-ated with them. Part of what the users of a language know (unconsciously) about the language is the types of ‘thing’ that are likely to fill a PR such as the Agent in


72 Robin P. Fawcett

a Process of “eating.” And this brings us to the crucial point. It is only possible to specify the types of ‘thing’ that are required in an economical way if the sys-tem network for noun senses is constructed on the basis of semantic criteria of the experiential type, i.e. if it is a cultural classification of “things.” It is this that enables all types of ‘food’ to be identified together within one sub-network, when the overall network is re-entered to generate the Affected in a Process of ‘eating.’

Unsurprisingly, perhaps, a system network that is able to express this type of preference coincides nicely with the concept that the network should reflect a generalized folk taxonomy of ‘things’ (as stated in Section 3.1).

Let us now look at the specific case of preselecting the Cognizant in a Process of ‘remembering.’ In this case the preselection rule restricts the choices so that the nominal group that is generated will typically (but not necessarily) be some type of human being. But how, precisely, is this done? It is by specifying that, before re-entering the network, the probabilities in the systems that might be entered have been re-set as follows:

[thing, physical_thing, living_thing, creature, 99.9% human / 0.1% non_human, whole_human, 99% individual_human / 1% group_of_humans].

In that example, the preselection is in most cases the absolute preference of 100%, this being represented by simply giving the name of the preferred feature. But at two points there is merely a relative preference for one feature over another. Note first that the feature [human] is shown to be a thousand times more probable than [non_human] – so allowing for the possibility of animals remembering things, even though we don’t often refer to this. (The possibility that the “rememberer” could be a computer could be handled by adding a similar set of preferences, following [physical_thing]). Thus individual humans are shown to be a hundred times more likely to be a ‘rememberer’ than are groups of humans, while recogniz-ing that a group of people such as a committee may occasionally remember things. Finally, note that any features in the network not mentioned in the rule retain their original probabilities, so that there is no preference among the different types of human than are specified in Figures 3 and 4. (Metaphorical uses of language fall outside the scope of this description, but see Fawcett (2012) for a summary of the approach to metaphor adopted in the Cardiff Grammar for text analysis, and Fawcett (forthcoming 2015a) for a generative account).

To summarize: the most frequent use of the system network for noun senses in generating text-sentences occurs when the probabilities on the semantic features from which the unit that will fill the PR will be generated get temporarily re-set. The fact that this can be done in probabilistic terms, as it is here, is a significant advance on the “all or nothing” rules for “selectional restrictions” that were used the transformational models of language in the 1960s and 1970s (e.g. Chomsky, 1965).



We turn next to an aspect of language that raises a serious question about the way in which this network should be structured. It is typified by the distinction in English between ‘count’ and ‘mass’ nouns – but the question is more complex than that, as we shall see, in at least two ways.

3.4 The problem of the ‘count’ versus ‘mass’ distinction and related matters

3.4.1 The problem of ‘count’ versus ‘mass’One important effect of deciding to organize the network for noun senses around their experiential meanings is this: it forces the grammarian to re-think the place in the lexicogrammar of the distinction between ‘count’ nouns and ‘mass’ nouns. Although it is prominent in English and in many other European languages, there are different but essentially equivalent distinctions in other languages, as we shall see in Section 3.5.

It is a surprisingly common assumption among those working in artificial intelligence that there should be a choice between ‘mass’ and ‘count’ at or near the start of such taxonomies – whether they are viewing it as a semantic system network within the lexicogrammar, as here, or as an ontology (or as the same thing, which is not uncommon). (See Fawcett, 1994 for a summary of the partially intertwined histories of system networks and ontologies). In other words, such researchers appear to assume that, because English makes a distinction between the two major classes of “count nouns” and “mass nouns” (e.g. in the quantifying expressions that may co-occur with them), this contrast should be made a primary distinc-tion in semantic system networks and ontologies. The next sub-section shows why that approach results in an inadequate system network, and the following one describes the simple solution to the problem that we have implemented in the Cardiff Grammar.

Let us illustrate the problem by considering a case in which we are generating a clause in which the Process is one of ‘putting on’ some additional clothing, as in “He went home and put on …” The question is: What is the best way to show the preferences for the type thing that the Agent puts on – i.e. the Affected? The key point is that there is no way of predicting whether this ‘thing’ is to be ‘mass’ or ‘count,’ e.g. “some warm clothing” or “a warm jersey.” The problem is that, if ‘mass’ vs. ‘count’ were to be made a primary distinction in the network, the grammar would require an unnecessarily complex realization rule to state the preference for any type of clothing, because the two types of clothing would be located in different parts of the network. And even if we were to accept this as the price to be paid, this would be inadequate, because in the full network there are no fewer than four different types (as we shall shortly see).


74 Robin P. Fawcett

If possible, then, we would like the lexicogrammar to model our ability to predict what types of ‘thing’ will fill a Participant Role such as the Agent and the Affected in a Process such as ‘putting on,’ while respecting the facts relating to their realizations as ‘count’ and ‘mass’ and other such types of noun.

Finally, we should note that, while the generalized preferences associated with a PR are often sufficient (as is often the case with an Agent), at other times the grammar needs supplementary preferences that are generated by the same seman-tic feature that generates the particular Process – as with the Affected in a Process of ‘eating’ food or ‘putting on’ clothing. And these cases too call for a system net-work that is organized on semantic criteria.

3.4.2 Two further problems with ‘mass’ versus ‘count’ as a primary systemHowever, there are two further problems that should dissuade us from prioritizing the ‘mass’ vs. ‘count’ distinction.

The first is the small set of nouns that are inherently plural, including “alms,” “cattle,” “clothes,” “congratulations,” “contents,” “goods,” “police,” “savings,” “staff ” and “thanks.” These can never be ‘singular’ so, since the grammar requires ‘count’ nouns to enter the system of ‘singular’ vs. ‘plural,’ we need to introduce a third cat-egory, i.e. ‘plural only.’ (Note that some nouns that may at first appear to be ‘plural only’ typically also serve in British English as ‘singular’ nouns, e.g. “crossroads,” “gallows,” “headquarters” and “services.” Such nouns function as a type of ‘count’ noun that has an irregular “zero plural” (as also do certain types of animals that are hunted or reared, such as “salmon,” “deer,” “sheep,” etc.)).

The second additional category that we need to recognize is the set of nouns that denote objects that are inherently a pair. Again, these have no ‘singular’ equivalent. These include “briefs,” “binoculars,” “braces,” “glasses,” “jeans,” “long-johns,” “overalls,” “pants,” “pliers,” “scissors,” “shorts,” “slacks,” “spectacles,” “tights,” “tongs,” “tweezers” and “trousers.” What distinguishes them from ‘plural only’ nouns is that, even when we place “a pair of ” before them, they still denote a single object.6 In the Cardiff Grammar these are referred to as ‘pair only’ nouns. (Notice that when we refer to two or more of them, we pluralize the item “pair,” as in “two pairs of trousers”).

So the belief that there is a neat ‘count’ versus ‘mass’ distinction in English is simply wrong.

Let us conclude this sub-section with an expansion of the example with which we began the previous sub-section. As before, the challenge is to exemplify the

6. Note that these examples are different from glove, shoe, sock, stocking etc., which are simply ‘count’ nouns denoting objects that often occur in pairs and that therefore have a greater prefer-ence than other ‘count’ nouns for being quantified by a pair.



available ways to complete “He went home and put on …” The final nominal group could be one that has at its head (i) a ‘mass’ noun as in “some warm clothing”; (ii) a ‘plural only’ noun as in “some warm clothes”; (iii) a ‘pair only’ noun as in “some warm trousers”; or (iv) a ‘count’ noun that can be either ‘singular,’ as in “a warm jersey,” or ‘plural,’ as in “two warm jerseys.”

Clearly, then, we need a system network that is organized on ‘cultural clas-sification’ criteria. But this leaves us with a new problem, which is: How can the grammar bring the many (or few) cases of each of the four types together, and so enable each to be identified by a single feature?

3.4.3 The solution to these apparent problemsWhy should this be so important? It is because it turns out that the system net-work for the cultural classification of ‘things’ is not, as is often assumed, a network in which the delicate features that are realized in lexical items are all terminal features. (That is still a common interpretation of Halliday’s 1961 “grammarian’s dream” that lexis might one day be modelled as “most delicate grammar” among those who haven’t worked in this area). The reason why features realized as nouns are not terminal features is that the grammar has to have a way in which the five features of ‘mass,’ ‘singular,’ ‘plural,’ ‘plural only’ and ‘pair only’ can lead on to a series of major system networks for generating quantifying determiners, as described in Fawcett (2007). In other words, significantly different versions of the network for quantification are entered, depending on which of those features has been chosen. This, then, is a prime example of “most delicate” lexical choices leading on further grammatical choices.7

The way in which this is modelled in the lexicogrammar is as follows. Every feature in the system network for which there is a realization is given one of four suffixes. The suffix “_c” is added to the terminal feature of all ‘count’ things, the suffix “_m” to all ‘mass’ things, “_pl” to all ‘plural only’ things and “_pair” to all ‘pair only’ things.

The thousands of features ending with “_c” then lead, in what is in effect a gigantic disjunctive entry condition of the type illustrated in Figure 6, into the system for number, where the choice is between [singular_cc] and [plural_cc]. And the effect is that one of these gets added to the growing selection expression from which the nominal group that realizes the current referent will be generated.8

7. Interestingly, the same situation occurs in the other two main classes of lexical items: lexical verbs and adjectives, as is demonstrated in Fawcett 1996 and Tucker 1997 respectively.

8. Because the disjunctive entry condition to the NUMBER system contains so many features (several thousand so far) it is modelled as follows: a special rule looks for the suffix “_c” among the features generated so far, and takes it as the entry condition to the NUMBER system. This


76 Robin P. Fawcett

Then in due course the realization rule for [plural_cc] adds a plural suffix of “s” or “es” to most nouns (with the rest being referred to the table for irregular nouns, as described for [phenomenon_c] in Section 3.2.1). And the features with the suf-fix “_m,” “_pl” and “_pair” become the entry conditions to system networks for quantification that are appropriate to their needs.

In the Cardiff Grammar it is even possible to build in the preference for mean-ings realized in expressions such as “a pair,” followed by “of,” as in “a pair of trou-sers.” This is because “trousers” rather than “pair” is treated as the head of the nominal group. The words “a pair” are generated as a nominal group that fills the quantifying determiner.

In the many ways described here, then, the Cardiff Grammar reflects the true, complex nature of ‘number’ and ‘quantity’ in English, while maintaining the semantic relationships of hyponymy and contrast in the network – and so making possible the expression of preferences for semantically related ‘things’ (such as ‘clothing’ or ‘food’) and for appropriate types of quantification.

3.4.4 Long thin things and other such grammatically realized categoriesThe issue that we have been considering is in fact much wider than that of whether [mass] v [count] should be a primary distinction in the system network for noun senses in English and related languages. To broaden our horizons, let us consider some of the other languages of the world.

Consider Chinese, with its well-known classifier system, in which the [count] v [mass] distinction plays no part. Then consider Swahili, with its “ki- vi-” class of non-living things, its “m- wa-” class for humans, its “u-” class for abstract things, and so on. And even stranger (from an English standpoint) is the fact that Japanese has a special set of cardinal determiners, whose form depends on the semantic class of the relevant noun: i.e. whether the object is a ‘human’ or a ‘small thing’ or even, it would seem, a ‘long thin thing.’ Thus, if the referent is classified as a flower (“hana”), a tree (“ki”), a pen (“pen”), a pencil (“enpitsu”) or a river (“kawa”) – which are all deemed to be ‘long thin things’ – the quantifying deter-miner meaning ‘one’ will be “ippon.” But if the thing is a human it is “hitori” and if a non-human creature it is “ipipi” – and so on, for more classes of thing and more cardinal determiners. It seems that what unites the things that require “ippon” is simply that they are all ‘long thin things.’

approach brings the enormous practical advantage that, as the network is extended, each new ‘count’ feature is automatically added to the entry condition. The same principle is used for ‘mass,’ ‘plural only’ and ‘pair only’ noun senses.



If this seems strange to a someone who is approaching Japanese from the background of a Germanic or a Romance language, consider how peculiar it must seem to the brain of a native speaker of Chinese, Japanese or Swahili when they first encounter English (or a related language), with its strangely prominent dis-tinction between things that are ‘countable’ and things that are not!

There is nothing sacrosanct, then, about the distinction between [mass] and [count] things that should induce us to give it priority in the system network for noun senses.

3.5 Some important non-taxonomic features in the system network

We turn now to two new types of feature that introduce certain important rela-tionships which we have ignored up to this point, but which occur throughout the network.

As we have seen, the systemic notation typically signifies the ‘type of ’ relation-ship. We shall call the features between which this relationship holds the substan-tive features of the network. But the grammar must also represent two sorts of relationship that may hold between substantive features other than the ‘type of ’ sort. To model these we shall introduce two new types of system, and so two new types of feature.

These are both illustrated in Figure 7, with examples of their realizations being given on the right. Here, then, ‘x’ stands for a feature such as ‘human,’ and ‘a,’ ‘b’ and ‘c’ are the next substantive features.

x_as_such (e.g. person)

whole_x a (e.g. adult)

b (e.g. adolescent)

x c (e.g. child)

part_of_x (e.g. hand)

group_of_x (e.g. committee)

Figure 7. Some types of features that are not related by ‘type of ’ relations.

In principle, either or both of these two types of system may occur between any two substantive features. But in most cases neither occurs, and the network moves directly to another “type of ” system with substantive features.


78 Robin P. Fawcett

The first system in Figure 7 provides for the situation in which the next sub-stantive feature denotes an entity that is something other than a whole ‘thing.’ The first of the two variants occurs when the referent is a part (or parts) of a ‘thing,’ such as a hand, as in “The bullet grazed his hand.”

The second variant occurs when the referent is a ‘group’ of ‘things,’ such as a committee, as in “The committee has decided to appoint Ivy as the new Principal.” This is the type of feature from which “collective nouns” such as “herd” (for sheep or cattle) and “school” (for dolphins) are generated, as well as less restricted ‘group’ nouns such as “collection,” “team” and several dozens more. Any such system will always contain a feature equivalent to [whole_x] and one or both of [part_of_x] or [group_of_x] in Figure 7. And in all such systems the general probability that [whole_x] will be selected is very high.

Now let us turn to the dependent system. Note first the feature [x_as_such]. This is the option to generate the noun that corresponds to the preceding sub-stantive feature, rather than going on to specify the referent in terms of a more delicate sub-category. It is features such as [fish_as_such] that enable the gram-mar to generate generic nouns such as “thing,” “stuff ” and “creature,” and, more delicately, “animal,” “fish” and “plant.” Note that without them the grammar could not generate such items.

You might think that we could achieve the same result by simply adding a realization rule to a substantive feature such as ‘fish’ to generate “fish.” But we cannot, because if we did the grammar would generate two nouns to expound the head of the nominal group, e.g. “fishtrout.” In other words, we need to ensure that the realization rule for generating “fish” is attached to a terminal feature (i.e. terminal in the present network). Without the “as such” feature the gram-mar would continue working its way through the network until it found a more delicate terminal feature, and from this it would generate a second noun. So it is through the many “as such” features in the network that we ensure that the grammar generates heads of nominal groups with a single noun, i.e. “fish” rather than “fishtrout.”

In Figure 7 the only feature shown that contrasts with [x_as_such] is [x-specified]. But there are in fact many different ways of specifying things, and Figures 3 and 4 illustrate two of the various types of specification that are often spelled out more fully, i.e. [specified_by_stage_and_gender] and [specified_by_role]. So there is often more than one [specified_by_x] feature in contrast with [x_as_such], with each leading to its own dependent network.

The example below (which takes the simplified networks in Figures and 3 and Figure 4 a step further in delicacy) illustrates both of the new types of system. It also illustrates (i) the rich variety of ways for specifying a ‘human’ and (ii) the fact that these only apply to things that are ‘whole_x’ (as Figure 7 suggests).



human → whole_human / part_of_human / group_of_humans. whole_human → specified_by_stage_and_gender / specified_by_role / specified_

by_nationality / etc. specified_by_stage_and_gender → adult / adolescent / child. specified_by_role → family_role / interpersonal_role / role_by_behaviour / work_ role / etc.

We can illustrate the size of these sub-networks by noting that, in the full network, there are 66 noun senses in just the sub-network for ‘family_role’ (without includ-ing the recursion of “great great grandfather,” etc.). Note, then, that the simple little “non-substantive” systems that we have been examining often lead on to large and varied areas of the meaning potential of English.

3.6 Other types of meaning in the network: Affective, register and dialect

Finally, we shall note briefly three other types of meaning that the network han-dles. The first is affective meaning, i.e. meanings that reflect the Performer’s emo-tional attitude (so feelings) – typically directed towards the referent. The meaning of some English nouns is purely ‘affective,’ as in “He’s an absolute bastard,” but many realize both experiential and affective meaning, such as “hero,” “coward,” “idiot,” etc., and those identified in the last four features in the system below.

woman → 80% woman_as_such_c (73.01) / 15% laudatory_of_woman_for_social behaviour_c (73.011) / 4.5% derogatory_of_woman_for_immaturity_c (73.012) / 0.1% derogatory_of_woman_for_age_c (73.013) / 0.4% derogatory_of_woman_for_promiscuity_c (73.014).

And here are the realization rules for the last four features:

73.011: laudatory_of_woman_for_social behaviour_c : h < “lady.”73.012: derogatory_of_woman_for_immaturity_c : h < “girl.”73.013: derogatory_of_woman_for_age_c : h < “hag.”73.014: derogatory_of_woman_for_promiscuity_c : h < “slag.”

(We shall come to the realization rule for [woman_as_such_c] shortly). A rather different type of meaning that is realized in variations in lexical form

is variations in register (reflecting aspects of the context of situation). Realization rule 73.01 for [woman_as_such_c], which is given below, attempts to model some of these for one of the most complex areas of meaning in English lexis. To interpret this rule you may like to know that the Cardiff Grammar recognizes four variables


80 Robin P. Fawcett

in the tenor of discourse, i.e. [very_formal], [formal], [consultative] and [casual]; four in technicality of discourse, two being used below; and two in mode of discourse, [spoken] and [written], neither being used here, however.

Now please examine the realization rule, and see if you can work out what it is saying – and, indeed, if you think it is on the right lines. (Note that “irrn” is an instruction to go to the table of irregular nouns to read off the ‘singular’ or ‘plural’ form, as needed).

73.01: woman_as_such_c : if (formal or very_formal) and not (strongly_technical or semi_technical) then h < “lady,” if consultative then “irrn”(woman_c), if casual then ( if (strongly_technical or semi_technical) then “irrn”(woman_c) else (if am_english then h < “doll” else h < “bird”) ).

Finally, note that the grammar can handle variations in dialect as well as regis-ter. For example, the last line of Rule 73.01 models the “knowledge” that when an Englishman of a particular age, social class and area refers to a woman in a casual register as a “bird,” an equivalent American might well say “doll.” (Note that such expressions change rapidly, and these rules reflect speech patterns acquired around, let us say, the 1970s).

In summary, we can say that the system network for the cultural classification of noun senses (and their related realization rules) makes provision for a very wide variety of types of meaning. It goes far beyond “type of ” relationships between substantive semantic features. In so doing it maps territory that is well beyond the nearest equivalent coverage in the frameworks of other theories of language – unless there is work out there that is unknown to us.9

9. I should perhaps remind you at this point that the “Upper Model” in the Penman Project is not a semantic system network but an ontology of concepts of the type described in Section 2.2; for this see Bateman 1990.



4. Summary and conclusions

Perhaps the best way to summarise this fairly long paper on this complex topic is to invite you to flip through the pages again, noting the section headings, figures and examples. This will remind you of the following: (i) the very different func-tions that are served by an ontology and a semantic system network for noun senses, (ii) the size and the scope of the Cardiff semantic system network for the cultural classification of ‘things,’ (iii) the reasons why the overall structure of the system network is based on experiential semantic criteria; (iv) the central role of general probabilities in the network itself and the way in which they are changed to generate nouns that are appropriate to the Process of the clause above; (v) the novel way in which the problem of modelling the ‘mass’ vs. ‘count’ distinc-tion and related distinctions is handled; (vi) the central role of two types of non-taxonomic relationships; (vii) the three non-experiential types of meaning that the network models; and (viii) the key role of the realization rules through which the terminal features of this system network generate the nouns that expound the heads of nominal groups. With nearly 5,000 noun senses, it is almost certainly the largest system network of its kind in existence today.

If you would like a copy of the full network at the current point in its develop-ment please email me to request it, at [email protected]. It you would then like to explore the possibility of contributing to its further development, please contact me to discuss this.

As it happens, the Cardiff Grammar can also claim to provide the fullest systemic functional descriptions of the three other major lexical word-classes of English. Here I shall simply give the sources for those interested in learning more about them, and rough estimates of their sizes.

The second largest set of word-forms in English is the lexical verbs, and the fullest descriptions to date of our networks for these can be found in Neale (2002a) and Neale (2002b). The latter is a data base that specifies (i) around 5,000 different lexical verb senses (including many realized in “multi-word” verbs), (ii) exam-ples, (iii) their systemic features and (iv) their associated Participant Roles. It can usefully be supplemented by Fawcett (2010) or the relevant portion of Fawcett (forthcoming 2015c). For a published description of the simplified system network designed for use in text analysis, see Fawcett (2011).

For the system network from which the overlapping meanings of ‘adjectives’ and ‘manner adverbs’ are generated, see Tucker (1998), which includes networks that specify around 200 ‘qualities’ of ‘things’ (realized as adjectives, e.g. “clever”), and also many related ‘qualities’ of ‘situations’ (e.g. “cleverly”), and describes all aspects of their associated structures.

mailto:[email protected]


82 Robin P. Fawcett

We saw in Section 3.2.2 that the network for noun senses already contains approaching 5,000 distinct word senses. If we add to these the meanings of lexical verbs, adjectives and manner adverbs that have been included so far in the Cardiff Grammar, the total number of word senses for lexis is over 12,000. And if we now add the meanings of the lexicogrammar’s very large vocabulary of ‘grammatical’ items (including ‘usuality’ adverbs, prepositions, modal verbs, cardinals, other types of quantifying expression, ordinals, co-ordinating and subordinating con-junctions, etc.) the total number of ‘word senses’ specified by the lexicogrammar will be over 14,000.

For a crude comparison, this total may be compared with the 14,700 lexi-cal and grammatical word forms in the Collins COBUILD English Dictionary (Sinclair 1995, p. xiii). The latter total, however, is merely the total of COBUILD’s word forms. So, since the COBUILD headwords typically distinguish several word senses (and occasionally over a dozen), it is clear that even the Cardiff Grammar, large though its coverage already is, still has some way to go before it can match that of COBUILD. The situation is therefore that, while the system networks of the Cardiff Grammar are probably the most comprehensive systemic resource currently available, and while we may claim to have addressed and solved many of the problems of constructing such networks, it is clear that there is still much further work to be done.

Perhaps it is time for younger scholars to carry this project forward? If they were to do so, we think Chris Butler would approve!

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The cultural classification of ‘things’: A system network for English noun senses

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