Event extraction is a particularly challenging type of information extraction (IE). Most current event extraction systems rely on local information at the phrase or sentence level. However, this local context may be insufficient to resolve ambiguities in identifying particular types of events; information from a wider scope can serve to resolve some of these ambiguities.
In this thesis, we first investigate how to extract supervised and unsupervised features to improve a supervised baseline system. Then, we present two additional tasks to show the benefit of wider scope features in semi-supervised learning (self training) and active learning (co-testing). Experiments show that using features from wider scope can not only aid a supervised local event extraction baseline system, but also help the semi-supervised or active learning approach.
|Commitee:||Davis, Ernest, Grishman, Ralph, Meyers, Adam, Sekine, Satoshi, Subramanian, Lakshminarayanan|
|School:||New York University|
|School Location:||United States -- New York|
|Source:||DAI-B 74/01(E), Dissertation Abstracts International|
|Keywords:||Event extraction, Information extraction, Machine learning, Natural language processing|
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