Probabilistic Approaches to Learning the Semantics and Syntax of Words

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We have billions of words of online text available at our fingertips, yet tools for effectively processing it - going beyond merely finding documents, to truly understanding them - are sorely lacking. A primary obstacle is the inherent flexibility of words. People are continually extending the meaning of words and combining them productively in previously unseen ways. Current natural language processing (NLP) systems, however, rely largely on static lexical resources - electronic dictionaries and ontologies - that are unable to support the needed flexibility and adaptability for understanding text. This proposal focuses on computational methods for learning rich semantic and syntactic knowledge about words, using robust probabilistic representations that capture the flexibility of words and are adaptable to new usages.