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Opinion Retrieval Experiments Using Generative Models: Experiments for the TREC 2007 Blog Track

9 years 10 months ago
Opinion Retrieval Experiments Using Generative Models: Experiments for the TREC 2007 Blog Track
Ranking blog posts that express opinions regarding a given topic should serve a critical function in helping users. We explored a couple of methods for opinion retrieval in the framework of probabilistic language models. The first method combines topic-relevance model and opinion-relevance model, at document level, that captures topic dependence of the opinion expressions. The second method combines the aforementioned topic-opinion relevance models at sentence level, and accumulates the negative cross entropy between the combined relevance models and each sentence model to obtain a document-level score. This paper reports the overview of our methods and the evaluation results on the Opinion Retrieval Task at the TREC 2007 Blog Track.
Yuki Arai, Koji Eguchi
Added 07 Nov 2010
Updated 07 Nov 2010
Type Conference
Year 2007
Where TREC
Authors Yuki Arai, Koji Eguchi
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