Parse reranking with WordNet using a hidden variable model
Name
56994000-MIT.pdf
Description
Full printable version
Size
10.73 MB
Format
Adobe PDF
Checksum (MD5)
a28e8f302ffc6f3774a0f0614bc0c7bf
Author(s)
Koo, Terry, 1981-
Advisor(s)
Michael Collins.
Date Issued
2004
Publisher
Massachusetts Institute of Technology
Abstract
We present a new parse reranking algorithm that extends work in (Michael Collins and Terry Koo 2004) by incorporating WordNet (Miller et al. 1993) word senses. Instead of attempting explicit word sense disambiguation, we retain word sense ambiguity in a hidden variable model. We define a probability distribution over candidate parses and word sense assignments with a feature-based log-linear model, and we employ belief propagation to obtain an efficient implementation. Our main results are a relative improvement of [approximately] 0.97% over the baseline parser in development testing, which translated into a [approximately] 0.5% improvement in final testing. We also performed experiments in which our reranker was appended to the (Michael Collins and Terry Koo 2004) boosting reranker. The cascaded system achieved a development set improvement of [approximately] 0.15% over the boosting reranker by itself, but this gain did not carry over into final testing.
Description
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 79-80).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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