Semantic Representations of Near-Synonyms for Automatic Lexical Choice

Semantic Representations of Near-Synonyms for Automatic Lexical Choice Philip Edmonds Doctor of Philosophy Graduate Department of Computer Science University of Toronto 1999 We develop a new computational model for representing the fine-grained meanings of nearsynonyms and the differences between them. We also develop a sophisticated lexical-choice process that can decide which of several near-synonyms is most appropriate in any particular context. This research has direct applications in machine translation and text generation, and also in intelligent electronic dictionaries and automated style-checking and document editing. We first identify the problems of representing near-synonyms in a computational lexicon and show that no previous model adequately accounts for near-synonymy. We then propose a preliminary theory to account for near-synonymy in which the meaning of a word arises out of a context-dependent combination of a context-independent core meaning and a set of explicit differences to its near-synonyms. That is, near-synonyms cluster together. After considering a statistical model and its weaknesses, we develop a clustered model of lexical knowledge, based on the conventional ontological model. The model cuts off the ontology at a coarse grain, thus avoiding an awkward proliferation of language-dependent concepts in the ontology, and groups near-synonyms into subconceptual clusters that are linked to the ontology. A cluster acts as a formal usage note that differentiates near-synonyms in terms of finegrained aspects of denotation, implication, expressed attitude, and style. The model is general enough to account for other types of variation, for instance, in collocational behaviour. We formalize various criteria for lexical choice as preferences to express certain concepts with varying indirectness, to express attitudes, and to establish certain styles. The lexical-choice process chooses the near-synonym that best satisfies the most preferences. The process uses an approximate-matching algorithm that determines how well the set of lexical distinctions of each near-synonym in a cluster matches a set of input preferences. We implemented the lexical-choice process in a prototype sentence-planning system. We evaluate the system to show that it can make the appropriate word choices when given a set of preferences.