acl acl2010 acl2010-164 acl2010-164-reference knowledge-graph by maker-knowledge-mining
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Author: Xu Sun ; Jianfeng Gao ; Daniel Micol ; Chris Quirk
Abstract: This paper explores the use of clickthrough data for query spelling correction. First, large amounts of query-correction pairs are derived by analyzing users' query reformulation behavior encoded in the clickthrough data. Then, a phrase-based error model that accounts for the transformation probability between multi-term phrases is trained and integrated into a query speller system. Experiments are carried out on a human-labeled data set. Results show that the system using the phrase-based error model outperforms cantly its baseline systems. 1 signifi-
Agichtein, E., Brill, E. and Dumais, S. 2006. Improving web search ranking by incorporating user behavior information. In SIGIR, pp. 19-26. Ahmad, F., and Kondrak, G. 2005. Learning a spelling error model from search query logs. In HLT-EMNLP, pp 955-962. Brill, E., and Moore, R. C. 2000. An improved error model for noisy channel spelling correction. In ACL, pp. 286-293. Chen, Q., Li, M., and Zhou, M. 2007. Improving query spelling correction using web search results. In EMNLP-CoNLL, pp. 181-189. Church, K., Hard, T., and Gao, J. 2007. Compressing trigram language models with Golomb coding. In EMNLP-CoNLL, pp. 199-207. Cucerzan, S., and Brill, E. 2004. Spelling correction as an iterative process that exploits the collective knowledge of web users. In EMNLP, pp. 293-300. Gao, J., Yuan, W., Li, X., Deng, K., and Nie, J-Y. 2009. Smoothing clickthrough data for web search ranking. In SIGIR. Golding, A. R., and Roth, D. 1996. Applying winnow to context-sensitive spelling correction. In ICML, pp. 182-190. Joachims, T. 2002. Optimizing search engines using clickthrough data. In SIGKDD, pp. 133-142. Kernighan, M. D., Church, K. W., and Gale, W. A. 1990. A spelling correction program based on a noisy channel model. In COLING, pp. 205-210. Koehn, P., Och, F., and Marcu, D. 2003. Statistical phrase-based translation. In HLT/NAACL, pp. 127-133. Kukich, K. 1992. Techniques for automatically correcting words in text. ACM Computing Surveys. 24(4): 377-439. Levenshtein, V. I. 1966. Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady, 10(8):707-710. Li, M., Zhu, M., Zhang, Y., and Zhou, M. 2006. Exploring distributional similarity based models for query spelling correction. In ACL, pp. 1025-1032. Mangu, L., and Brill, E. 1997. Automatic rule acquisition for spelling correction. In ICML, pp. 187-194. Och, F. 2002. Statistical machine translation: from single-word models to alignment templates. PhD thesis, RWTH Aachen. Och, F., and Ney, H. 2004. The alignment template approach to statistical machine translation. Computational Linguistics, 30(4): 417-449. Okazaki, N., Tsuruoka, Y., Ananiadou, S., and Tsujii, J. 2008. A discriminative candidate generator for string transformations. In EMNLP, pp. 447-456. Philips, L. 1990. Hanging on the metaphone. Computer Language Magazine, 7(12):38-44. Suzuki, H., Li, X., and Gao, J. 2009. Discovery of term variation in Japanese web search queries. In EMNLP. Toutanova, K., and Moore, R. 2002. Pronunciation modeling for improved spelling correction. In ACL, pp. 144-15 1. Wang, X., and Zhai, C. 2008. Mining term association patterns from search logs for effective query reformulation. In CIKM, pp. 479-488. Whitelaw, C., Hutchinson, B., Chung, G. Y., and Ellis, G. 2009. Using the web for language independent spellchecking and autocorrection. In EMNLP, pp. 890-899. 274