2026-09-18T20:14:05Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/284312022-01-13T07:54:29Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Michael Collins.
Koo, Terry, 1981-
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2005-09-26T20:24:36Z
2005-09-26T20:24:36Z
2004
2004
http://hdl.handle.net/1721.1/28431
56994000
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (p. 79-80).
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.
by Terry Koo.
M.Eng.
80 p.
3723304 bytes
3731773 bytes
application/pdf
application/pdf
en_US
Massachusetts Institute of Technology
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http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Parse reranking with WordNet using a hidden variable model
Thesis
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<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.</Abstract>
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