Parse reranking with WordNet using a hidden variable model
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
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| Format: | Thesis |
| Language: | en_US |
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Massachusetts Institute of Technology
2005
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| Online Access: | http://hdl.handle.net/1721.1/28431 |
| _version_ | 1826194656922173440 |
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| author | Koo, Terry, 1981- |
| author2 | Michael Collins. |
| author_facet | Michael Collins. Koo, Terry, 1981- |
| author_sort | Koo, Terry, 1981- |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004. |
| first_indexed | 2024-09-23T09:59:55Z |
| format | Thesis |
| id | mit-1721.1/28431 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:59:55Z |
| publishDate | 2005 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/284312019-04-11T06:37:57Z Parse reranking with WordNet using a hidden variable model Koo, Terry, 1981- Michael Collins. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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. 2005-09-26T20:24:36Z 2005-09-26T20:24:36Z 2004 2004 Thesis http://hdl.handle.net/1721.1/28431 56994000 en_US M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 80 p. 3723304 bytes 3731773 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Koo, Terry, 1981- Parse reranking with WordNet using a hidden variable model |
| title | Parse reranking with WordNet using a hidden variable model |
| title_full | Parse reranking with WordNet using a hidden variable model |
| title_fullStr | Parse reranking with WordNet using a hidden variable model |
| title_full_unstemmed | Parse reranking with WordNet using a hidden variable model |
| title_short | Parse reranking with WordNet using a hidden variable model |
| title_sort | parse reranking with wordnet using a hidden variable model |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/28431 |
| work_keys_str_mv | AT kooterry1981 parsererankingwithwordnetusingahiddenvariablemodel |