CAREER: Reference Resolution for Natural Language Understanding

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A major obstacle in building robust systems that extract and interpret information, and summarize and answer questions from texts, is the need to identify the entities referred to by pronouns or other referential expressions. This project extends the PI's prior work involving the development of an empirical reference resolution system that relies on several sets of heuristics that correspond to various forms of reference. In particula, the framework will be extended to learn semantic knowledge that supports consistency checks. This enhancement will provide high precision reference resolution and also enhance substantially the recall of referential links. The research will be evaluated using referenceannotated texts and the Penn Treebank corpora. The outcome will be a corpus-based method for reference resolution for both pronouns and nominal expressions. First, the semantics of all referential noun phrases will be captured. Then, by extending the empirical environment with bootstrapping, this reference resolution technique should lead to a powerful tool capable of resolving reference correctly in a large variety of texts. Finally, the tool will be incorporated both in an information extraction system and in a question/answering system, to measure its contribution to the overall performance of these systems. The proposed research departs from previous approaches to reference resolution, in that it promotes data-driven techniques instead of relying on combinations of linguistic and cognitive aspects of language. The immediate pragmatic outcome indicated by the preliminary results should be a substantial recall enhancement.