Automatic acquisition and use of lexical semantic information

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Recent natural language processing (NLP) applications---such as web searching and information extraction---have achieved impressive results by adopting `knowledge-lean' approaches that treat word semantics very superficially.  Nonetheless, we are frequently left with a feeling of frustration that these systems do not truly understand our queries or the documents being processed.  Building systems that can automatically determine the meaning of electronic text at a deeper level requires more sophisticated linguistic knowledge about words, yet hand coding such knowledge is infeasible on a broad scale.The research proposed here has two primary goals.  The first goal is to develop methods for the automatic acquisition of lexical knowledge, in order to better capture the complex information encoded in words, as well as their interrelationships.  We adopt a `knowledge-informed' approach that is distinguished by the incorporation of linguistic and psychological observations into novel techniques for statistical corpus analysis, enabling us to alleviate the knowledge-acquisition bottleneck in NLP.  The second goal of the research is to apply the same knowledge-informed perspective within the processing of lexical resources, to ensure that computational lexicons and word/concept ontologies can be used more effectively and with less manual effort.In the long run, research on automatic acquisition of deeper lexical information from text corpora, and techniques for more effectively using the resulting computational lexicons, will revolutionize the use of computers.  Even in the short term, the research proposed here will lead to improved NLP applications, such as web searching based on actual meaning rather than simple keyword matching, and information extraction that can be applied beyond circumscribed types of extracted information within a narrow domain.