1996 DATRaLanguageforLexicalKnowledg

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Subject Headings: DATR Lexical KR Language.

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Abstract

Much recent research on the design of natural language lexicons has made use of nonmonotonic inheritance networks as originally developed for general knowledge representation purposes in Artificial Intelligence. DATR is a simple, spartan language for defining nonmonotonic inheritance networks with path/value equations, one that has been designed specifically for lexical knowledge representation. In keeping with its intendedly minimalist character, it lacks many of the constructs embodied either in general-purpose knowledge representation languages or in contemporary grammar formalisms. The present paper shows that the language is nonetheless sufficiently expressive to represent concisely the structure of lexical information at a variety of levels of linguistic analysis. The paper provides an informal example-based introduction to DATR and to techniques for its use, including finite-state transduction, the encoding of DAGs and lexical rules, and the representation of ambiguity and alternation. Sample analysis of phenomena such as inflectional syncretism and verbal subcategorization are given that show how the language can be used to squeeze out redundancy from lexical descriptions.

1. Introduction

Irregular lexemes are standardly regular in some respect. Most are just like regular lexemes except that they deviate in one or two characteristics. What is needed is a natural way of saying "this lexeme is regular except for this property." One obvious approach is to use nonmonotonicity and inheritance machinery to capture such lexical irregularity (and subregularity), and much recent research into the design of representation languages for natural language lexicons has thus made use of nonmonotonic inheritance networks (or “semantic nets") as originally developed for more general representation purposes in Artificial Intelligence. [[Daelemans, De Smedt, and Gazdar (1992)]] provide a rationale for, and an introduction to, this body of research and we will not rehearse the content of that paper here, nor review the work cited there. [1] DATR is a rather spartan nonmonotonic language for defining inheritance networks with path/value equations. In keeping with its intendedly minimalist character, it lacks many of the constructs embodied either in general-purpose knowledge representation languages or in contemporary grammar formalisms. But the present paper seeks to show that the language is nonetheless sufficiently expressive to represent concisely the structure of lexical information at a variety of levels of language description.

The development of DATR has been guided by a number of concerns, which we summarize here. Our objective has been a language that (i) has an explicit theory of inference, (ii) has an explicit declarative semantics, (iii) can be readily and efficiently implemented, (iv) has the necessary expressive power to encode the lexical entries presupposed by work in the unification grammar tradition, and (v) can express all the evident generalizations and subgeneralizations about such entries. Our first publications on DATR (Evans and Gazdar 1989a, 1989b) provided a formal theory of inference (i) and a formal semantics (ii) for DATR and we will not recapitulate that material here. [2]

With respect to (iii), the core inference engine for DATR can be coded in a page of Prolog (see, e.g., Gibbon 1993, 50). At the time of writing, we know of a dozen different implementations of the language, some of which have been used with large [[DATR Lexicon|DATR lexicon]]s in the context of big NLP systems (e.g., Andry et al. 1992; Cahill 1993a, 1994; Cahill and Evans 1990). We will comment further on implementation matters in Section 5, below. However, the main purpose of the present paper is to exhibit the use of DATR for lexical description (iv) and the way it makes it relatively easy to capture lexical generalizations and subregularities at a variety of analytic levels (v). We will pursue (iv) and (v) in the context of an informal example-based introduction to the language and to techniques for its use, and we will make frequent reference to the DATR-based lexical work that has been done since 1989.

The paper is organized as follows: Section 2 uses an analysis of English verbal morphology to provide an informal introduction to DATR. Section 3 describes the language more precisely: its syntax, inferential and default mechanisms, and the use of abbreviatory variables. Section 4 describes a wide variety of DATR techniques, including case constructs and parameters, Boolean logic, finite-state transduction, lists and DAGs, lexical rules, and ways to encode ambiguity and alternation. Section 5 explores more technical issues relating to the language, including functionality and consistency, multiple-inheritance, modes of use, and existing implementations. Section 6 makes some closing observations. Finally, an appendix to the paper replies to the points made in the critical literature on DATR.

2. DATR by Example

We begin our presentation of DATR with a partial analysis of morphology in the English verbal system. In DATR, information is organized as a network of nodes, where a node is essentially just a collection of closely related information. In the context of lexical description, a node typically corresponds to a word, a lexeme, or a class of lexemes. For example, we might have a node describing an abstract verb, another for the subcase of a transitive verb, another for the lexeme love, and still more for the individual words that are instances of this lexeme (love, loves, loved, loving, etc.). Each node has associated with it a set of path/value pairs, where a path is a sequence of atoms (which are primitive objects), and a value is an atom or a sequence of atoms. We will sometimes refer to atoms in paths as attributes.

For example, a node describing the present participle form of the verb love (and called perhaps Wordl) might contain the path/value pairs shown in Table 1. The paths in this example all happen to contain two attributes, and the first attribute can be thought of as distinguishing syntactic and morphological types of information. The values indicate appropriate linguistic settings for the paths for a present participle form of love. Thus, its syntactic category is verb, its syntactic type is main (i.e., it is a main verb, not an auxiliary), its syntactic form is present p a r t i c i p l e (a two-atom sequence), its morphological form is love ing (another two-atom sequence). In DATR this can be written as: [3]

Table 1 Path/value pairs for present participle of love.  Path Value  syn cat verb  syn type main  syn form present participle  mor form love ing in 
Wordl:
  <syn cat> = verb
  <syn type> = main
  <syn form> = present participle
  <mor form> = love ing.

Here, angle brackets (<...>) delimit paths. Note that values can be atomic or they can consist of sequences of atoms, as the two last lines of the example illustrate [4]. As a first approximation, nodes can be thought of as denoting partial functions from paths (sequences of atoms) to values (sequences of atoms), [5]

...

5.4 Implementations

As already noted, the inferential core of DATR is extremely simple to implement. We know of the existence of approximately a dozen different implementations of the language but there may well be others that we do not know of. The best known, and most widely available are our own (Brighton/Sussex), which is written in Prolog and runs on most Unix platforms, Gibbon's (Bielefeld) DDATR Scheme and NODE Sicstus Prolog implementations, and Kilbury's (Duesseldorf) QDATR Prolog implementation, which runs (in compiled form) on PCs and on Sicstus Prolog under Unix. All of these are freely available on request, as is an extensive archive of over one hundred example fragments, some of which illustrate formal techniques and others of which are applications of DATR to the lexical phonology, morphology, syntax, or semantics of a wide variety of different languages. 48 Other interesting implementations that we are familiar with include the experimental reverse query implementation by Langer (Osnabrueck), Duda and Gebhardi's (Berlin) implementation that is dynamically linked to PATR, and Barg's (Duesseldorf) implementation of a system that induces DATR descriptions from extensional data sets.

6. Concluding Remarks

Our title for this paper is to be taken literally -- DATR is a language for lexical knowledge representation. It is a kind of programming language, not a theoretical framework for the lexicon (in the way that, say, HPSG is a theoretical framework for syntax). Clearly, the language is well suited to lexical frameworks that embrace, or are consistent with, nonmonotonicity and inheritance of properties through networks of nodes. But those two dispositions hardly constitute a restrictive notion of suitability in the context of contemporary NLP work, nor are they absolute requirements: it is, for example, entirely possible to write useful DATR fragments that never override inherited values (and so are monotonic) or that define isolated nodes with no inheritance.

It is true, of course, that our examples, in this paper and elsewhere, reflect a particular set of assumptions about how NLP lexicons can be best organized. But, apart from the utility of inheritance and nonmonotonicity, we have been careful not to build those assumptions into the [[DATR Language|DATR language]] itself. There is, for example, no built-in assumption that lexicons should be lexeme-based rather than, say, word- or morpheme-based.

Unlike some other NLP inheritance languages, DATR is not intended to provide the facilities of a particular syntactic formalism. Rather, it is intended to be a lexical formalism that can be used with any syntactic representation that can be encoded in terms of attributes and values. Thus, at the time of writing, we know of nontrivial [[DATR Lexicon|DATR lexicon]]s written for GPSG, I_TAG, PATR, Unification Categorial Grammar, and Word Grammar. Equally, the use of DATR does not commit one, in advance, to adopting any particular set of theoretical assumptions with respect to phonology, morphology, or semantics. In phonology, for example, the language allows one to write transducers that map strings of atomic phonemes to strings of atomic phones. But it also allows one to encode full-blown feature- and syllable-tree-based prosodic analyses. Unlike the formalisms typically proposed by linguists, DATR does not attempt to embody in its design any substantive and restrictive universal claims about the lexicons of natural language. That does not distinguish it from most NLP formalisms, of course. However, we have also sought to ensure that its design does not embody features that would restrict its use to a single language (English, say) or to a particular class of closely related languages (the Romance class, say). The available evidence suggests that we have succeeded in the latter aim since, at the time of writing, nontrivial DATR fragments of the lexicons of Arabic, Arapesh, Czech, English, French, German, Gikuyu, Italian, Latin, Polish, Portuguese, Russian, and Spanish have been developed. There are also smaller indicative fragments for Baoule, Dakota, Dan, Dutch, Japanese, Nyanja, Sanskrit, Serbo-Croat, Swahili, and Tem.

Unlike most other languages proposed for lexical knowledge representation, DATR is not intended to be restricted in the levels of linguistic description to which it can sensibly be applied. It is designed to be equally applicable at phonological, orthographic, morphological, syntactic, and semantic levels of description; but it is not intended to replace existing approaches to those levels. Rather, we envisage descriptions of different levels according to different theoretical frameworks being implementable in DATR: thus an NLP group might decide, for example, to build a lexicon with DRT-style semantic representations, H PSG-style syntactic representations, "item & arrangement"- style morphological representations and a KIMMO-style orthographic component, implementing all of these, including the HPSG lexical rules, in DATR. DATR itself does not mandate any of the choices in this example, but equally, nor does it allow such choices to be avoided. [6] DATR cannot be (sensibly) used without a prior decision as to the theoretical frameworks in which the description is to be conducted: there is no "default" framework for describing morphological facts in DATR. Thus, for example, Gibbon (1992) and Langer and Gibbon (1992) use DATR to implement their ILEX theory of lexical organization, Corbett and Fraser (1993) and Fraser and Corbett (in press) use DATR to implement their Network Morphology framework, and Gazdar (1992) shows how Paradigm Function Morphology analyses (Stump 1992) can be mapped into DATR. Indeed, it would not be entirely misleading to think of DATR as a kind of assembly language for constructing (or reconstructing) higher-level theories of lexical representation.

Footnotes

  1. Daelemans and Gazdar (1992) and Briscoe, de Paiva, and Copestake (1993) are collections that bring together much recent work on the application of inheritance networks to lexical description. Other relevant recent work not found there includes Bouma (1993), Briscoe, Copestake, and Lascarides (1995), Calder (1994), Copestake (1992), Daelemans (1994), Daelemans and De Smedt (1994), Ide, Le Maitre, and V6ronis (1994), Lascarides et al. (1996), Mellish and Reiter (1993), Mitamura and Nyberg (1992), Penn and Thomason (1994), Reiter and Mellish (1992), Young (1992), and Young and Rounds (1993).
  2. Note, however, that the definitions in the 1989 papers are not given in sufficient generality to cover [[DATR equation|DATR equation]]s with more than one (non-atomic) descriptor on the right hand side. Keller (1995) effectively replaces our 1989 presentation of a semantics for DATR and his treatment is general enough to cover descriptor sequences
  3. The syntax of DATR, like its name and its minimalist philosophy, owes more than a little to that of the unification grammar language PATR (Shieber 1986). With hindsight this may have been a bad design decision since similarity of syntax tends to imply a similarity of semantics. And, as we shall see in Section 4.7 below, and elsewhere, there is a subtle but important semantic difference.
  4. Node names and atoms are distinct, but essentially arbitrary, classes of tokens in DATR. In this paper we shall distinguish them by a simple case convention--node names start with an uppercase letter, atoms do not.
  5. This is an approximation since it ignores the role of global contexts, see Section 5.1, below.
  6. However, DATR's framework-agnosticism may make it a plausible candidate for the construction of polytheoretic lexicons. For example, one that would allow either categorial or HPSG-style subcategorization specifications to be derived, depending on the setting of a parameter.

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
1996 DATRaLanguageforLexicalKnowledgRoger Evans
Gerald Gazdar
DATR: A Language for Lexical Knowledge Representation1996
AuthorRoger Evans + and Gerald Gazdar +
titleDATR: A Language for Lexical Knowledge Representation +
year1996 +