Semantic-based opinion retrieval using predicate-argument structures and subjective adjectives
Document Type
Conference Proceeding
Publication Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
We present the results of our experiment on the use of predicate-argument structures containing subjective adjectives for semantic-based opinion retrieval. The approach exploits the grammatical tree derivation of sentences to show the underlying meanings through the respective predicate-argument structures. The underlying meaning of each subjective sentence is then semantically compared with the underlying meaning of the query topic given in natural language sentence. Rather than using frequency of opinion words or their proximity to query words, our solution is based on frequency of semantically related subjective sentences. We formed a linear relevance model that uses explicit and implicit semantic similarities between predicate-argument structures of subjective sentences and the given query topic. Thus, the technique ensures that opinionated documents retrieved are not only subjective but have semantic relevance to the given query topic. Experimental results show that the technique improves performance of topical opinion retrieval task. © 2011 Springer-Verlag Berlin Heidelberg.
First Page
372
Last Page
385
DOI
10.1007/978-3-642-25631-8_34
Publication Date
12-28-2011
Recommended Citation
Orimaye, Sylvester Olubolu; Alhashmi, Saadat M.; and Eu-Gene, Siew, "Semantic-based opinion retrieval using predicate-argument structures and subjective adjectives" (2011). Global Population Health Faculty Publications. 30.
https://doi.org/10.1007/978-3-642-25631-8_34
https://collections.uhsp.edu/global-population-health_pubs/30