this paper, I will discuss four major topics relating to current research in lexical semantics: methodology, descriptive coverage, adequacy of the representation, and the computational usefulness of representations. In addressing these issues, I will discuss what I think are some of the central problems facing the lexical semantics community, and suggest ways of best approaching these issues. Then, I will provide a method for the decomposition of lexical categories and outline a theory of lexical semantics embodying a notion of cocompositionality and type coercion, as well as several levels of semantic description, where the semantic load is spread more evenly throughout the lexicon. I argue that lexical decomposition is possible if it is performed generatively. Rather than assuming a fixed set of primitives, I will assume a fixed number of generative devices that can be seen as constructing semantic expressions. I develop a theory of Qualia Structure, a representation language for lexical items, which renders much lexical ambiguity in the lexicon unnecessary, while still explaining the systematic polysemy that words carry. Finally, I discuss how individual lexical structures can be integrated into the larger lexical knowledge base through a theory of lexical inheritance. This provides us with the necessary principles of global organization for the lexicon, enabling us to fully integrate our natural language lexicon into a conceptual whole

In Latent Semantic Analysis (LSA) the meaning of a word is represented as a vector in a high-dimensional semantic space. Different meanings of a word or different senses of a word are not distinguished. Instead, word senses are appropriately modified as the word is used in different contexts. In N-VP sentences, the precise meaning of the verb phrase depends on the noun it is combined with. An algorithm is described to adjust the meaning of a predicate as it is applied to different arguments. In forming a sentence meaning, not all features of a predicate are combined with the features of the argument, but only those that are appropriate to the argument. Hence, a different "sense" of a predicate emerges every time it is used in a different context. This predication algorithm is explored in the context of four different semantic problems: metaphor interpretation, causal inferences, similarity judgments, and homonym disambiguation.

A bi-polar theory of the structure and function of nominals and clauses is presented in which a specifier, functioning as a referential pole, and a head, functioning as a relational pole, combine to form a referring expression. The theory applies to both object referring expressions, in the case of nominals, and situation referring expressions, in the case of clauses. The bi-polar theory is contrasted with X-Bar Theory—a uni-polar theory in which the head uniquely determines the type of the larger expression in which it occurs. Uni-polar theories adopt a strong notion of endocentricity, which is rejected in the bi-polar theory, where both the specifier and the head make significant and meaningful contributions to the larger expressions in which they occur. The bi-polar theory is also contrasted with Langacker’s conception of the basic structure and function of nominals and clauses.

The fact that concepts are grammaticalized di#erently in different languages is a major problem for translation, especially for machine translation. Two major examples of this are syntactic number, and the use of (in)definite articles (a, some, the). In languages such as English, nouns are marked for number and the choice of article (or of no article) must be made for every noun phrase. In contrast, for languages such as Japanese, number distinctions are not normally made, and there are no articles. This means that whenever a noun phrase is translated from Japanese to English, even if the denotation is perfectly understood and a good translation equivalent found, generating the noun phrase still requires two difficult choices: should the head noun be singular or plural, and which article, if any, should be generated. This thesis proposes a semantic representation and a series of three heuristic algorithms that make possible the appropriate generation of articles and number when translating from Japanese to English. The semantic representation provides a tractable set of features to represent (1) the referential use of a noun phrase, as either referential, generic, ascriptive or idiomatic; (2) the interpretation of the noun phrase's referent as either a countable individual or a mass, with seven detailed subtypes; (3) the definiteness of the noun phrase, as either definite, indefinite, definite and extensive, or possessed. The three algorithms automatically acquire values for these features from the analysis of the Japanese text and the lexical properties of the English translation equivalents, and then use them to generate English. The first algorithm determines the referential use of Japanese noun phrases, based on a defeasible hierarchy of pragmatic rules that are applie...

In this paper we will look into questions that concern what may be considered two of the central meaning relations in semantics, i.e. polysemy or the association of multiple meanings with one form and synonymy, i.e. the association of one meaning with multiple forms.

Abstract not found

Abstract not found

Abstract not found

a diachronic perspective

U Collective Animal or Human = (Collective + O) V Plant or Animal = (P + A) W Inanimate Concrete or Abstract = (T + I) X Abstract or Human = (T + H) Y Abstract or Animate = (T + H) 80 CHAPTER 3. LEXICAL SEMANTIC RESOURCES Z Unmarked 1 Human or Solid = (H + S) 2 Abstract or Solid = (T + S) 4 Abstract Physical 5 Organic Material 6 Liquid or Abstract = (L + T) 7 Gas or Liquid = (G + L) The basic codes are organized into the hierarchy shown in Figure 3.1 Most noun senses have # # # # ## # # # # # ## # # # # # ## # # # # # ### # # # # # ## # # # ## # # # ## # # # ## # # # ## # # # ## # # # ## # # # # # ### # # # # # # ### # # # # ## # # ## # # # # # # # # # # # ## M(Male) F(Female) D(Male) B(Female) N(Not Movable) J(Movable) H(Human) P(Plant) A(Animal) G(Gas) L(Liquid) S(Solid) Q(Animate) I(inanimate) 4(Abstract Physical) C(Concrete) T(Abstract) TOP Figure 3.1: Hierachy of semantic codes in LDOCE ...