Dissertation/Thesis Abstract

Acquiring Information from Wider Scope to Improve Event Extraction
by Liao, Shasha, Ph.D., New York University, 2012, 103; 3524290
Abstract (Summary)

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.

Indexing (document details)
Advisor: Grishman, Ralph
Commitee: Davis, Ernest, Grishman, Ralph, Meyers, Adam, Sekine, Satoshi, Subramanian, Lakshminarayanan
School: New York University
Department: Computer Science
School Location: United States -- New York
Source: DAI-B 74/01(E), Dissertation Abstracts International
Subjects: Computer science
Keywords: Event extraction, Information extraction, Machine learning, Natural language processing
Publication Number: 3524290
ISBN: 978-1-267-58586-8
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