University of Texas Rio Grande Valley TREC LiveQA 2016: Using Topic Modeling to Answer Complex Questions
نویسنده
چکیده
Abstract This paper describes the system submitted to the TREC 2016 LiveQA track. This year, the TREC 2016 LiveQA track consists of implementing a web service to answer user-submitted questions. The newest unanswered question from Yahoo! Answers will be posted to the web service, a question every minute for 24 hours. The implementation described in this paper uses natural language processing (NLP) to extract keywords from the question given as input. A web search together with a Yahoo! Answer search is used to select candidate answers. A latent dirichlet allocation (LDA) model is trained in order to compute a topic distribution of the different candidate answers. Finally, the Jensen-Shannon distance is used as similarity measure between the candidate answers and the question given as input. This implementation performed better than the average scores.
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