Learning to reformulate long queries
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
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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/1721.1/60164 |
| _version_ | 1826193175238148096 |
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| author | Gupta, Neha, S.M. Massachusetts Institute of Technology |
| author2 | Tommi Jaakkola. |
| author_facet | Tommi Jaakkola. Gupta, Neha, S.M. Massachusetts Institute of Technology |
| author_sort | Gupta, Neha, S.M. Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. |
| first_indexed | 2024-09-23T09:34:54Z |
| format | Thesis |
| id | mit-1721.1/60164 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T09:34:54Z |
| publishDate | 2010 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/601642019-04-11T06:03:39Z Learning to reformulate long queries Gupta, Neha, S.M. Massachusetts Institute of Technology Tommi Jaakkola. 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, 2010. Includes bibliographical references (p. 82-86). Long search queries are useful because they let the users specify their search criteria in more detail. However, the user often receives poor results in response to the long queries from today's Information Retrieval systems. For the document to be returned as a relevant result, the system requires every query term to appear in the document. This makes the search task especially challenging for those users who lack the domain knowledge or have limited search experience. They face the difficulty of selecting the exact keywords to carry out their search. The goal of our research is to help bridge that gap so that the search engine can help novice users formulate queries in a vocabulary that appears in the index of the relevant documents. We present a machine learning approach to automatically summarize long search queries, using word specific features that capture the discriminative ability of particular words for a search task. Instead of using hand-labeled training data, we automatically evaluate a search query using a query score specific to the task. We evaluate our approach using the task of searching for related academic articles. by Neha Gupta. S.M. 2010-12-06T17:32:06Z 2010-12-06T17:32:06Z 2010 2010 Thesis http://hdl.handle.net/1721.1/60164 681759824 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 86 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Gupta, Neha, S.M. Massachusetts Institute of Technology Learning to reformulate long queries |
| title | Learning to reformulate long queries |
| title_full | Learning to reformulate long queries |
| title_fullStr | Learning to reformulate long queries |
| title_full_unstemmed | Learning to reformulate long queries |
| title_short | Learning to reformulate long queries |
| title_sort | learning to reformulate long queries |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/60164 |
| work_keys_str_mv | AT guptanehasmmassachusettsinstituteoftechnology learningtoreformulatelongqueries |