Semantic-Based Opinion Retrieval Using Predicate-Argument Structures and Subjective Adjectives

摘要

We present the results of our experiment on the use of predicateargument 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.