Abstract
To support the personalization of Question Answering (QA) systems, we propose a new probabilistic scoring approach based on the topics of the question and candidate answers. First, a set of topics of interest to the user is learned based on a topic modeling approach such as Latent Dirichlet Allocation. Then, the similarity of questions asked by the user to the candidate answers, returned by the search engine, is estimated by calculating the probability of the candidate answer given the question. This similarity is used to re-rank the answers returned by the search engine. Our preliminary experiments show that the reranking highly increases the performance of the QA system estimated based on accuracy and MRR (mean reciprocal rank).
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Chinaei, H., Lamontagne, L., Laviolette, F., Khoury, R. (2014). A Topic Model Scoring Approach for Personalized QA Systems. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2014. Lecture Notes in Computer Science(), vol 8655. Springer, Cham. https://doi.org/10.1007/978-3-319-10816-2_11
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DOI: https://doi.org/10.1007/978-3-319-10816-2_11
Publisher Name: Springer, Cham
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