Using predicate-argument structures for context-dependent opinion retrieval
Document Type
Conference Proceeding
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
Current opinion retrieval techniques do not provide context-dependent relevant results. They use frequency of opinion words in documents or at proximity to query words, such that opinionated documents containing the words are retrieved regardless of their contextual or semantic relevance to the query topic. Thus, opinion retrieved for the qualitative analysis of products, performance measurement for companies, and public reactions to political decisions can be largely biased. We propose a sentence-level linear relevance model that is based on subjective and semantic similarities between predicate-argument structures. This ensures opinionated documents are not only subjective but semantically relevant to the query topic. The linear relevance model performs a linear combination of a popular relevance model, our proposed transformed terms similarity model, and a popular subjectivity mechanism. Evaluation and experimental results show that the use of predicate-argument structures improves performance of opinion retrieval task by more than 15% over popular TREC baselines. © 2011 Springer-Verlag.
First Page
386
Last Page
402
DOI
10.1007/978-3-642-25856-5_29
Publication Date
12-28-2011
Recommended Citation
Orimaye, Sylvester Olubolu; Alhashmi, Saadat M.; and Eu-Gene, Siew, "Using predicate-argument structures for context-dependent opinion retrieval" (2011). Global Population Health Faculty Publications. 29.
https://doi.org/10.1007/978-3-642-25856-5_29
https://collections.uhsp.edu/global-population-health_pubs/29