Can predicate-argument structures be used for contextual opinion retrieval from blogs?

Document Type


Publication Title

World Wide Web


We present the results of our investigation on the use of predicate-argument structures for contextual opinion retrieval. The use of predicate-argument structure for opinion retrieval is a novel approach that exploits the grammatical derivation of sentences to show contextual and subjective relevance. We do not use frequency of certain keywords as it is usually done in keyword-based opinion retrieval approaches. Rather, our novel solution is based on frequency of contextually relevant and subjective sentences. We use a linear relevance model that leverages semantic similarities among predicate-argument structures of sentences. Thus, this paper presents the evaluation results of the linear relevance model. The model does a linear combination of a popular relevance model, our proposed transformed terms similarity model, and the absolute value of a sentence subjectivity scoring scheme. The predicate-argument structures are derived from the grammatical derivations of natural language query topics and the well formed sentences from blog documents. The derived predicate-argument structures are then semantically compared to compute an opinion relevance score. Our scoring technique uses the highest frequency of semantically related predicate-argument structures enriched with the total subjectivity score from sentences. Evaluation and experimental results show that predicate-argument structures can indeed be used for contextual opinion retrieval as it improves performance of opinion retrieval task by 15% over the popular TREC baselines. © 2012 Springer Science+Business Media, LLC.

First Page


Last Page




Publication Date