Abstract

Introduction

Investigations within the generative perspective aim at modeling, by means of a small number of rules, principles and constraints, linguistic phenomena (either morphological, syntactic or semantic) at a high level of abstraction, level that seems to be appropriate for research on multilinguism and language learning. These works, among other things, attempt at modeling a certain form of “creativity” in language: from a limited number of linguistic resources, a potentially infinite set of surface forms can be generated.

Among works within the generative perspective, let us concentrate on the Generative Lexicon (Pustejovsky, 1991, 1995), which has settled in the past years one of the most innovative perspective in lexical semantics. This approach introduces an abstract model radically opposed to “flat” sense enumeration lexicons. This approach, which is now well-known, is based (1) on the close cooperation of three lexical semantic structures: the argument structure (including selectional restrictions), the aspectual structure, and the Qualia structure; (2) on a detailed type theory and a type coercion procedure; and (3) on a refined theory of compositionality.

Lexical semantics is still in a rather early stage of development. This explains the reason why there are relatively few elaborated systems for representing its various aspects. A number of semantic aspects can be straightforwardly represented by means of feature-value pairs and by means of typed feature structures. Others, such as the Qualia Structure or the lexical semantics relations are more difficult to represent. The difficulty is twofold: (1) there is first the need to define appropriate models to represent the various levels of semantic information, including their associated possible inference systems and their properties (e.g., transitivity, monotonicity, etc.) and (2) there is the need to develop complex algorithms that allow for as efficient as possible treatments from these models. The first chapter of this section tackles the first point and the second one addresses some algorithmic problems.

“Introducing Lexlog” by J. Jayez, is a set of specifications for constructing explicit representations for lexical objects in restricted domains. Lexlog offers two types of functions: control functions to formalize representations and updatings on these representations in a controlled way, and expression functions to express different semantic operators and to tailor these operators with syntactic operators, for example, the trees of the Tree Adjoining Grammar framework. The discussion ends with a detailed presentation of the implementation in Prolog.

The last chapter, “Constraint propagation techniques for lexical semantics descriptions,” by Patrick Saint-Dizier, addresses the problem of the propagation in parse trees of large feature structures. The motivation is basically to avoid computations of intermediate results which later turn out to be useless.

Another current major issue in lexical semantics is the definition and the construction of real-size lexical databases that will be used by parsers and generators in conjunction with a grammatical system. Word meaning, terminological knowledge representation and extraction of knowledge in machine readable dictionaries are the main topics addressed. They really represent the backbone of a lexical semantics knowledge base construction.

The first chapter, “Lexical semantics and terminological knowledge representation” by Gerrit Burkert, shows the practical and formal inadequacies of semantic networks for representing knowledge, and the advantages of using a term subsumption language. In a first stage, this document shows how several aspects of word meaning can be adequately described using a term subsumption language. Then, some extensions are proposed that make the system more suitable for lexical semantics. Formal aspects are strongly motivated by several examples borrowed from an in-depth study of terminological knowledge extraction, which is a rather challenging area for lexical semantics.

“Word meaning between lexical and conceptual structure”, by Peter Gerstl, presents a method and a system to introduce world-knowledge or domain-dependent knowledge in a lexicon. The meaning of a word is derived from general lexical information on the one hand and from ontological knowledge on the other hand. The notion of semantic scope is explored on an empirical basis by analyzing in a systematic way the influences involved in natural language expressions. This component has been integrated into the Lilog system developed at IBM Stuttgart.

This volume on computational lexical semantics emerged from a workshop on lexical semantics issues organized in Toulouse, France, in January 1992. The chapters presented here are extended versions of the original texts.

Lexical semantics is now becoming a major research area in computational linguistics and it is playing more of a central role in various types of applications involving natural language parsers as well as generators.

Lexical semantics covers a wide spectrum of problematics from different disciplines, from psycholinguistics to knowledge representation and to computer architecture, which makes this field relatively difficult to perceive as a whole. The goal of this volume is to present the state of the art in lexical semantics from a computational linguistics point of view and from a range of perspectives: psycholinguistics, linguistics (formal and applied), computational linguistics, and application development. The following points are particularly developed in this volume:

• psycholinguistics: mental lexicons, access to lexical items, form of lexical items, links between concepts and words, and lexicalizing operations;

• linguistics and formal aspects of lexical semantics: lexical semantics relations, prototypes, conceptual representations, event structure, argument structure, and lexical redundancy;

• knowledge representation: systems of rules, treatment of type coercion, aspects of inheritance, and relations between linguistics and world knowledge;

• applications: creation and maintenance of large-size lexicons, the role of the lexicon in parsing and generation, lexical knowledge bases, and acquisition of lexical data;

• operational aspects: processing models and architecture of lexical systems.

Lexical semantics offers a large variety of uses in natural language processing and it obviously allows for more refined treatments. One of the main problems is to identify exactly the lexical semantic resources that one needs to solve a particular problem. Another main difficulty is to know how best to organize this knowledge in order to keep the system reasonably efficient and maintainable; this is particularly crucial for a number of large-scale applications. This volume contains two chapters that explore application of lexical semantics in the area of natural language generation and in the area of machine translation with an interlingua representation.

The first chapter of this section, “Lexical functions of the Explanatory Combinatorial Dictionary for lexicalization in text generation”, by Margarita Alonso Ramos et al., applies Mel'čuk's framework to natural language generation. It shows that the problem of lexicalization, i.e., the relation between a concept (or a combination of concepts) and its linguistic realization, cannot really be correctly carried out without making reference to a lexicon that takes into account the diversity of the lexico-semantics relations. This approach views lexicalization both as a local process (lexicalization is solved within a restricted phrase) and a more global one, taking into account the ‘contextual effects’ of a certain lexicalization with respect to the others in a sentence or in a text. Paradigmatic lexical functions are shown to be well adapted to treat lexicalization in the context of a text, whereas syntagmatic ones operate at the sentence or proposition levels.

Introduction

Recent works in Computational Linguistics show the central role played by the lexicon in language processing, and in particular by the lexical semantics component. Lexicons tend no longer to be a mere enumeration of feature-value pairs but tend to have an intelligent behavior. This is the case, for example, for generative lexicons (Pustejovsky, 1991) which contain, besides feature structures, a number of rules to create new (partial) definitions of word-senses such as rules for conflation and type coercion. As a result, the size of lexical entries describing word-senses has substantially increased. These lexical entries become very hard to be used directly by a natural language parser or generator because their size and complexity allow a priori little flexibility.

Most natural language systems consider a lexical entry as an indivisible whole which is percolated up in the parse/generation tree. Access to features and feature values at grammar rule level is realized by more or less complex procedures (Shieber, 1986; Johnson, 1990; Günthner, 1988). The complexity of real natural language processing systems makes such an approach very inefficient and not necessarily linguistically adequate. In this document, we propose a dynamic treatment of features in grammars, embedded within a Constraint Logic Programming framework (noted as CLP hereafter) which permits us to access a feature-value pair associated to a certain word-sense directly into the lexicon, and only when this feature is explicitly required by the grammar, for example, to make a check. More precisely, this dynamic treatment of features will make use of both constraint propagation techniques embedded within CLP and CLP resolution mechanisms.

Lexical semantics knowledge often requires various kinds of inferences to be made in order to make more precise and more explicit the meaning of a word with respect to the context in which it is uttered. The treatment of inheritance is particularly crucial from this point of view since linguistic as well as conceptual hierarchies allow us to relate a word with more generic terms that contribute, in a certain sense, to the definition of the meaning of that word. Because hierarchies are complex, inheritance often involves non-monotonic forms of reasoning. Another relation with artificial intelligence is the development of models and techniques for the automatic acquisition of linguistic knowledge.

In “Blocking”, Ted Briscoe et al. elaborate on the introduction of default inheritance mechanisms into theories of lexical organization. Blocking is a kind of inference rule that realizes a choice when two kinds of inheritances can be drawn from resources occurring at different levels in the lexical organization. For example, a general morphological rule adds -ed at the end of verbs for the past construction. A subclass of verbs consists of irregular verbs, which receive a particular mark for the past. A default rule states that the exceptional mark is preferred to the general case. However there exist verbs such as dream, which accept both flections (dreamt, dreamed). Then the default rule is no longer adequate and a more flexible solution is required. In this chapter, a framework based on a non-monotonic logic that incorporates more powerful mechanisms for resolving conflicts of various kinds is introduced.

The next part in this book concerns linguistic issues for lexical semantics. The main issues addressed are based on the notion of Generative Lexicon (Pustejovsky, 1991) and its consequences for the construction of lexicons.

The first chapter, “Linguistic constraints on type coercion,” by James Pustejovsky, summarizes the foundations of the Generative Lexicon which he defined a few years ago. This text investigates how best to characterize the formal mechanisms and the linguistic data necessary to explain the behavior of logical polysemy. A comprehensive range of polymorphic behaviors that account for the variations in semantic expressiveness found in natural languages is studied.

Within the same formal linguistic paradigm, we then have a contribution by Sabine Bergler, “From lexical semantics to text analysis,” which illustrates several issues of the Generative Lexicon using data from the Wall Street Journal. This chapter addresses in depth an important issue of the Generative Lexicon: what kind of methods can be used to create Generative Lexicon lexical entries with precise semantic content for the Qualia roles of the Generative Lexicon, from linguistic analysis. Special attention is devoted to the production of partial representations and to incremental analysis of texts.

The next chapter, “Lexical functions, generative lexicons and the world” by Dirk Heylen, explores the convergences and divergences between Mel'čuk's analysis of lexical functions and the generative lexicon approach. The author then proposes an interesting and original knowledge representation method based on lexical functions mainly following Mel'čuk's approach.

Introduction

In this chapter, we present a synopsis of several notions of psycholinguistics and linguistics that are relevant to the field of lexical semantics. We mainly focus on the notions or theoretical approaches that are broadly used and admitted in computational linguistics. Lexical semantics is now playing a central role in computational linguistics, besides grammar formalisms for parsing and generation, and sentence and discourse semantic representation production. The central role of lexical semantics in computational linguistics can be explained by the fact that lexical entries contain a considerable part of the information that is related to the word-sense they represent.

This introduction will provide the reader with some basic concepts in the field of lexical semantics and should also be considered as a guide to the chapters included in this book. We first present some basic concepts of psycholinguistics which have some interest for natural language processing. We then focus on the linguistic aspects which are commonly admitted to contribute substantially to the field. It has not, however, been possible to include all aspects of lexical semantics: the absence of certain approaches should not be considered as an a priori judgment on their value.

The first part of this text introduces psycholinguistic notions of interest to lexical semantics; we then present linguistic notions more in depth. At the end of this chapter, we review the chapters in this volume.

Contribution of psycholinguistics to the study of word meaning

Results from psycholinguistic research can give us a good idea of how concepts are organized in memory, and how this information is accessed in the mental lexicon.

The first topic, psycholinguistic and cognitive aspects of lexical semantics, is addressed in the first two chapters. This area is particularly active but relatively ignored in computational circles. The reasons might be a lack of precise methods and formalisms. This area is, however, crucial for the construction of well-designed semantic lexicons by the empirical psychologically based analysis introduced in the domain of computational lexical semantics.

“Polysemy and related phenomena from a cognitive linguistic viewpoint” by Alan Cruse surveys the ways in which the contribution of the same grammatical word makes to the meaning of a larger unit depending on the context. Two main explanatory hypotheses that account for contextual variation are explored: lexical semantics and pragmatics. Alternatives to this approach are studied in the other parts of the volume.

The second chapter, “Mental lexicon and machine lexicon: Which properties are shared by machine and mental word representations? Which are not?” by J.-F. Le Ny, sets language comprehension and interpretation within a general cognitive science perspective. Properties common to natural and artificial semantic units (e.g., denotation, super-ordination, case roles, etc.) are first explored. Then, problems related to activability and accessibility in the memory are addressed in both a theoretical and experimental way.