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S2ORM: exploiting syntactic and semantic information for opinion retrieval

Published: 16 April 2012 Publication History

Abstract

Opinion retrieval is the task of finding documents that express an opinion about a given query. A key challenge in opinion retrieval is to capture the query-related opinion score of a document. Existing methods rely mainly on the proximity information between the opinion terms and the query terms to address the key challenge. In this study, we propose to incorporate the syntactic and semantic information of terms into a probabilistic language model in order to capture the query-related opinion score more accurately.

References

[1]
S. Gerani, M. J. Carman, and F. Crestani. Proximity-Based Opinion Retrieval. In Proceedings of SIGIR '10, 2010.
[2]
L. Guo, F. Zhai, Y. Shao and X. Wan. PKUTM at TREC 2010 Blog Track. In Proceedings of TREC'10, 2010.
[3]
A. Moschitti. Making tree kernels practical for natural language learning. In Proceedings of EACL'06, 2006.
[4]
M. Steyvers and T. Griffiths. Probabilistic Topic Models. In Landauer, T., McNamara, D., Dennis, S., Kintsch, W., Latent Semantic Analysis: A Road to Meaning. 2006.

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  1. S2ORM: exploiting syntactic and semantic information for opinion retrieval

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    WWW '12 Companion: Proceedings of the 21st International Conference on World Wide Web
    April 2012
    1250 pages
    ISBN:9781450312301
    DOI:10.1145/2187980

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    • Univ. de Lyon: Universite de Lyon

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 16 April 2012

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    Author Tags

    1. opinion retrieval
    2. semantic relatedness
    3. syntactic tree

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    WWW 2012
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    • Univ. de Lyon
    WWW 2012: 21st World Wide Web Conference 2012
    April 16 - 20, 2012
    Lyon, France

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    Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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