Abstract
The answering communities, such as Yahoo! Answers, offer great intelligence to help people solve questions. Participants can express their judgements towards answers and the system also keeps a record for every user. Retrieving Question-Answer pairs (QA pairs) extracted from these forums can improve the quality of Question-Answering (QA) systems. In this paper, we propose a Collaborative Ranking (ColRank) algorithm employing the Continuous Markov Chain Model (CMCM) to combine the quality of QA pairs and relationships among them. Empirical results show that the innovative algorithm is effective and outperform the state of art Question-Answering baselines.
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References
Lin, D., Pantel, P.: Discovery of inference rules for question-answering. Natural Language Engineering 7(4), 734–749 (2001)
Hermjakob, U.: Parsing and question classification for question answering. In: Proceedings of the Workshop on Open-Domain Question Answering, vol. 12 (2001)
Lopez, V., Pasin, M., Motta, E.: AquaLog: An ontology-portable question answering system for the semantic web. In: Gómez-Pérez, A., Euzenat, J. (eds.) ESWC 2005. LNCS, vol. 3532, pp. 546–562. Springer, Heidelberg (2005)
Clarke, C.L.A., Cormack, G.V., Kemkes, G., Laszlo, M., Lynam, T.R., Terra, E.L., Tilker, P.L.: Statistical selection of exact answers. In: MultiText Experiments for TREC 2002 (2002)
Brill, E., Dumais, S., Banko, M.: An analysis of the askmsr question-answering system. In: EMNLP 2002: Proceedings of the ACL 2002 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 291–298 (2002)
Paranjpe, D., Ramakrishnan, G., Srinivasan, S.: Passage scoring for question answering via bayesian inference on lexical relations. In: The Twelfth Text REtrieval Conference, pp. 305–310 (2003)
Ding, S., Cong, G., Lin, C.-Y., Zhu, X.: Using conditional random fields to extract contexts and answers of questions from online forums. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, pp. 710–718 (2008)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Computer Networks 30(1-7), 107–117 (1998)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999)
Erkan, G., Radev, D.R.: Lexpagerank: Prestige in multi-document text summarization. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 365–371 (2004)
Otterbacher, J., Erkan, G., Radev, D.R.: Biased lexrank: Passage retrieval using random walks with question-based priors. Information Processing and Management 45, 42–54 (2009)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Communications of the ACM 18, 613–620 (1975)
Ilse, I., Wills, R.M.: Analysis and computation of google’s pagerank. In: 7th IMACS International Symposium on Iterative Methods in Scientific Computing
Jeon, J., Croft, W.B., Lee, J.H., Park, S.: A framework to predict the quality of answers with non-textual features. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 228–235 (2006)
Voorhees, E.: Overview of the trec 2001 question answering track. In Proceedings of the 10th Text Retrieval Conference (TREC 2010), pp. 157–165 (2001)
Li, X., Wang, Y.-Y., Acero, A.: Learning query intent from regularized click graphs. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 339–346 (2008)
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Wang, X., Zhang, M. (2011). Let Other Users Help You Find Answers: A Collaborative Question-Answering Method with Continuous Markov Chain Model. In: Du, X., Fan, W., Wang, J., Peng, Z., Sharaf, M.A. (eds) Web Technologies and Applications. APWeb 2011. Lecture Notes in Computer Science, vol 6612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20291-9_27
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DOI: https://doi.org/10.1007/978-3-642-20291-9_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20290-2
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