The precise meaning of each word that we encounter is modulated by the specific context in which it occurs. This aspect of lexical semantics is most clearly evident for ambiguous words: Individual word forms (e.g., run) map onto different interpretations in different sentence contexts (e.g., the athlete/politician/river runs). Models of word-meaning access must therefore explain how listeners and readers can rapidly settle on a single, contextually appropriate meaning for each word that they encounter. I will summarise an account of word-meaning access that places semantic disambiguation at its core and integrates a wide range of experimental evidence to explain this key aspect of language comprehension. The model places learning mechanisms at its heart. Learning plays a vital role in shaping and maintaining high-quality lexical-semantic knowledge throughout the life span.

As one of the sense alternations in natural language, regular polysemy has been extensively studied in lexical semantics, where its sense alternation is assumed to be systematic.

This talk will introduce our recent work in developing the Chinese Wordnet and a word sense tagger that handles regular polysemy.

Some challenges that arose from the co-predication phenomenon will be discussed.