A Topic Model Scoring Approach for Personalized QA Systems
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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).
KeywordsPersonalized QA User Modeling Topic Modeling
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- 2.Cai, L., Zhou, G., Liu, K., Zhao, J.: Learning the latent topics for question retrieval in community qa. In: Proceedings of 5th International Joint Conference on Natural Language Processing, Chiang Mai, Thailand (2011)Google Scholar
- 3.Celikyilmaz, A., Hakkani-Tur, D., Tur, G.: LDA based similarity modeling for question answering. In: The Conference of the North American Chapter of the Association for Computational Linguistics, NAACL HLT 2010, Workshop on Semantic Search, SS 2010, pp. 1–9. Association for Computational Linguistics, Los Angeles (2010), http://dl.acm.org/citation.cfm?id=1867767.1867768 Google Scholar
- 4.Ji, Z., Xu, F., Wang, B., He, B.: Question-answer topic model for question retrieval in community question answering. In: Proceedings of the 21st ACM International Conference on Information and knowledge Management, Maui, Hawaii, USA (2012)Google Scholar
- 6.Pala Er, N., Cicekli, I.: A factoid question answering system using answer pattern matching. In: Proceedings of the 6th International Joint Conference on Natural Language Processing, IJNLP 2013, Nagoya, Japan (October 2013), http://www.aclweb.org/anthology/I13-1106
- 7.Qu, M., Qiu, G., He, X., Zhang, C., Wu, H., Bu, J., Chen, C.: Probabilistic question recommendation for question answering communities. In: Proceedings of the 18th International Conference on World Wide Web (WWW 2009), Madrid, Spain (2009), http://doi.acm.org/10.1145/1526709.1526942
- 8.Schlaefer, N., Gieselmann, P., Schaaf, T., Waibel, A.: A pattern learning approach to question answering within the ephyra framework. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2006. LNCS (LNAI), vol. 4188, pp. 687–694. Springer, Heidelberg (2006), http://dx.doi.org/10.1007/11846406_86 CrossRefGoogle Scholar
- 9.Voorhees, E.M.: Overview of the TREC 2004 Question Answering Track. In: Text REtrieval Conference (TREC). Special Publication 500-261. National Institute of Standards and Technology, NIST (2004)Google Scholar