Text structure-aware classification
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2010
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| Online Access: | http://hdl.handle.net/1721.1/53315 |
| _version_ | 1811089707507908608 |
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| author | Dzunic, Zoran, Ph. D. Massachusetts Institute of Technology |
| author2 | Regina Barzilay. |
| author_facet | Regina Barzilay. Dzunic, Zoran, Ph. D. Massachusetts Institute of Technology |
| author_sort | Dzunic, Zoran, Ph. D. Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. |
| first_indexed | 2024-09-23T14:23:25Z |
| format | Thesis |
| id | mit-1721.1/53315 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T14:23:25Z |
| publishDate | 2010 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/533152019-04-10T14:52:58Z Text structure-aware classification Dzunic, Zoran, Ph. D. Massachusetts Institute of Technology Regina Barzilay. 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 (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009. Cataloged from PDF version of thesis. Includes bibliographical references (p. 73-76). Bag-of-words representations are used in many NLP applications, such as text classification and sentiment analysis. These representations ignore relations across different sentences in a text and disregard the underlying structure of documents. In this work, we present a method for text classification that takes into account document structure and only considers segments that contain information relevant for a classification task. In contrast to the previous work, which assumes that relevance annotation is given, we perform the relevance prediction in an unsupervised fashion. We develop a Conditional Bayesian Network model that incorporates relevance as a hidden variable of a target classifier. Relevance and label predictions are performed jointly, optimizing the relevance component for the best result of the target classifier. Our work demonstrates that incorporating structural information in document analysis yields significant performance gains over bag-of-words approaches on some NLP tasks. by Zoran Dzunic. S.M. 2010-03-25T15:30:16Z 2010-03-25T15:30:16Z 2009 2009 Thesis http://hdl.handle.net/1721.1/53315 550546800 eng 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 76 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Dzunic, Zoran, Ph. D. Massachusetts Institute of Technology Text structure-aware classification |
| title | Text structure-aware classification |
| title_full | Text structure-aware classification |
| title_fullStr | Text structure-aware classification |
| title_full_unstemmed | Text structure-aware classification |
| title_short | Text structure-aware classification |
| title_sort | text structure aware classification |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/53315 |
| work_keys_str_mv | AT dzuniczoranphdmassachusettsinstituteoftechnology textstructureawareclassification |