Liqiang Guo
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.
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Digital Library
- L. Guo, F. Zhai, Y. Shao and X. Wan. PKUTM at TREC 2010 Blog Track. In Proceedings of TREC'10, 2010.Google Scholar
- A. Moschitti. Making tree kernels practical for natural language learning. In Proceedings of EACL'06, 2006.Google Scholar
- 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.Google Scholar
Index Terms
- S2ORM: exploiting syntactic and semantic information for opinion retrieval
Recommendations
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