Abstract

The paper presents a concise description of a comprehensive approach to computational lexical semantics and focuses on the treatment of events. We reason about the semantic information that should be encoded in a lexicon entry to support the twin tasks of constructing Text Meaning Representations (TMRs) for input texts and generating texts off TMRs. As static knowledge sources cannot be expected to cover all textual inputs, we describe and illustrate how lexical entries can be changed dynamically to fit the textual context at processing time. On the very important issue of knowledge acquisition, our experience shows that determining the meaning of lexical items is not a trivial task for a team of human acquirers (who are, we believe, absolutely indispensable for the more complex decisions in lexical knowledge acquisition). We illustrate how one can overcome the subjectivity of acquirers partly through advanced methodology and partly by having the lexical-semantic model account for some of the combinatory and (semi-)productive principles of natural language.KeywordsNatural Language ProcessingMachine TranslationLexical ItemLexical EntryAmbiguous WordThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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