Semantic Web Dog Food

Presented at: 20th International World Wide Web Conference (WWW2011)

by Sylvester Olubolu Orimaye

Existing opinion retrieval techniques do not provide context-dependent relevant results. Most of the approaches used by state-of-the-art techniques are based on frequency of query terms, such that all documents containing query terms are retrieved, regardless of contextual relevance to the intent of the human seeking the opinion. However, in a particular opinionated document, words could occur in different contexts, yet meet the frequency attached to a certain opinion threshold, thus explicitly creating a bias in overall opinion retrieved. In this paper we propose a sentence-level contextual model for opinion retrieval using grammatical tree derivations and approval voting mechanism. Model evaluation performed between our contextual model, BM25, and language model shows that the model can be effective for contextual opinion retrieval such as faceted opinion retrieval.

Sentence-Level Contextual Opinion Retrieval was presented at this event.

Keywords: Text Mining, World Wide Web

Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2011/phd/sentence-level-contextual-opinion-retrieval

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