NDLI: Social annotation in query expansion: a machine learning approach

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Social annotation in query expansion: a machine learning approach

Content Provider	ACM Digital Library
Author	Lin, Yuan Ye, Zheng Lin, Hongfei Jin, Song
Abstract	Automatic query expansion technologies have been proven to be effective in many information retrieval tasks. Most existing approaches are based on the assumption that the most informative terms in top-retrieved documents can be viewed as context of the query and thus can be used for query expansion. One problem with these approaches is that some of the expansion terms extracted from feedback documents are irrelevant to the query, and thus may hurt the retrieval performance. In social annotations, users provide different keywords describing the respective Web pages from various aspects. These features may be used to boost IR performance. However, to date, the potential of social annotation for this task has been largely unexplored. In this paper, we explore the possibility and potential of social annotation as a new resource for extracting useful expansion terms. In particular, we propose a term ranking approach based on social annotation resource. The proposed approach consists of two phases: (1) in the first phase, we propose a term-dependency method to choose the most likely expansion terms; (2) in the second phase, we develop a machine learning method for term ranking, which is learnt from the statistics of the candidate expansion terms, using ListNet. Experimental results on three TREC test collections show that the retrieval performance can be improved when the term ranking method is used. In addition, we also demonstrate that terms selected by the term-dependency method from social annotation resources are beneficial to improve the retrieval performance.
Starting Page	405
Ending Page	414
Page Count	10
File Format	PDF
ISBN	9781450307574
DOI	10.1145/2009916.2009972
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-07-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Social annotation Query expansion Learning to rank
Content Type	Text
Resource Type	Article

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