Learning to reformulate long queries
Name
681759824-MIT.pdf
Description
Full printable version
Size
7.53 MB
Format
Adobe PDF
Checksum (MD5)
89f5ed8987ceae736ef21a352fa77be3
Author(s)
Gupta, Neha, S.M. Massachusetts Institute of Technology
Advisor(s)
Tommi Jaakkola.
Date Issued
2010
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Includes bibliographical references (p. 82-86).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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