Cross-lingual query suggestion

Data processing: database and file management or data structures – Database and file access – Search engines

Reexamination Certificate

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C707S748000, C707S749000, C707S760000, C704S001000, C704S008000, C704S009000

Reexamination Certificate

active

08051061

ABSTRACT:
Cross-lingual query suggestion (CLQS) aims to suggest relevant queries in a target language for a given query in a source language. The cross-lingual query suggestion is improved by exploiting the query logs in the target language. CLQS provides a method for learning and determining a similarity measure between two queries in different languages. The similarity measure is based on both translation information and monolingual similarity information, and in one embodiment uses both the query log itself and click-through information associated therewith. Monolingual and cross-lingual information such as word translation relations and word co-occurrence statistics may be used to estimate the cross-lingual query similarity with a discriminative model.

REFERENCES:
patent: 5301109 (1994-04-01), Landauer et al.
patent: 5787410 (1998-07-01), McMahon
patent: 5956740 (1999-09-01), Nosohara
patent: 6055528 (2000-04-01), Evans
patent: 6064951 (2000-05-01), Park et al.
patent: 6081774 (2000-06-01), de Hita et al.
patent: 6321189 (2001-11-01), Masuichi et al.
patent: 6321191 (2001-11-01), Kurahashi
patent: 6370498 (2002-04-01), Flores et al.
patent: 6381598 (2002-04-01), Williamowski et al.
patent: 6604101 (2003-08-01), Chan et al.
patent: 7146358 (2006-12-01), Gravano et al.
patent: 7149688 (2006-12-01), Schalkwyk
patent: 7194455 (2007-03-01), Zhou et al.
patent: 7260570 (2007-08-01), Brown et al.
patent: 7269598 (2007-09-01), Marchisio
patent: 7720856 (2010-05-01), Goedecke et al.
patent: 7809714 (2010-10-01), Smith
patent: 7814103 (2010-10-01), Gravano et al.
patent: 2001/0029455 (2001-10-01), Chin et al.
patent: 2002/0111792 (2002-08-01), Cherny
patent: 2004/0230417 (2004-11-01), Kraiss et al.
patent: 2005/0021323 (2005-01-01), Li
patent: 2005/0071152 (2005-03-01), Morimoto et al.
patent: 2005/0125215 (2005-06-01), Wu et al.
patent: 2005/0273318 (2005-12-01), Zhou et al.
patent: 2006/0009963 (2006-01-01), Gaussier et al.
patent: 2006/0173839 (2006-08-01), Knepper et al.
patent: 2006/0173886 (2006-08-01), Moulinier et al.
patent: 2006/0265209 (2006-11-01), Bradford
patent: 2007/0022134 (2007-01-01), Zhou et al.
patent: 2007/0027905 (2007-02-01), Warren et al.
patent: 2007/0214131 (2007-09-01), Cucerzan et al.
patent: 2008/0288474 (2008-11-01), Chin et al.
patent: 2009/0125497 (2009-05-01), Jiang et al.
Ballesteros et al., “Phrasal Translation and Query Expansion Techniques for Cross-Language Information Retrieval,” In Proceedings of the 20th Annual International ACM SIGIR, 1997, 8 pgs.
Burges et al, “Learning to Rank using Gradient Descent,” Proceedings ICML, 2005, Germany, pp. 89-96.
Cao et al, “Adapting Ranking SVM to Document Retrieval,” In Proceedings of SIGIR '06, 2006, 8 pgs.
Gao et al, “Cross-Lingual Query Suggestion Using Query Logs of Different Languages,” Proceedings of SIGIR '07, ACM, 2007, Amsterdam, 8 pgs.
Gao, et al., “Statistical Query Translation Models for Cross-Language Information Retrieval”, at <<http://research.microsoft.com/˜jfgao/paper/gao—nie—zhou.talip2006.rev.pdf>>, ACM, Dec. 2005, pp. 36.
Gleich et al., “SVD Subspace Projections for Term Suggestion Ranking and Clustering,” In Technical Report, Yahoo! Research Labs, 2004, 7 pgs.
Hull, “Using Statistical Testing in the Evaluation of Retrieval Experiments,” In Proc. SIGIR, 1993, 9 pgs.
Jang et al, “Using Mutual Information to Resolve Query Translation Ambiguities and Query Term Weighting,” ACM, 1999, pp. 223-229.
Jeon, et al, “Finding Similar Questions in Large Question and Answer Archives,” In Proc. CIKM, 2005, 7 pgs.
Lavrenko, et al., “Cross-Lingual Relevance Models,” In Proc. SIGIR, 2002, 8 pgs.
Lu et al, “Towards Web Mining of Query Translations for Cross-Language Information Retrieval in Digital Libraries,” ICADL, 2003, 12 pgs.
McNamee et al, “Comparing Cross-Language Query Expansion Techniques by Degrading Translation Resources,” In Proc. SIGIR 2002, pp. 159-166.
Nie et al, “Cross-Language Information Retrieval based on Parallel Texts and Automatic Mining of Parallel Texts from the Web,” SIGIR, 1999, 8 pgs.
Pingali, et al., “Experiments in Cross Language Query Focused Multi-Document Summarization”, available at least as early as Nov. 2, 2007, at <<http://search.iiit.ac.in/CLIA2007/papers/CLQSum.pdf>>, pp. 7.
Ponte et al, “A Language Modeling Approach to Information Retrieval,” In Proceedings of SIGIR '98, ACM, 1998, 7 pgs.
Robertson et al, “Okapi at TREC-3,” In ProceedingsTREC-3, 1995, 19 pgs.
Tur, et al., “Using Information Extraction to Improve Cross-lingual Document Retrieval”, available at least as early as Nov. 2, 2007, at <<http://www.cs.nyu.edu/hengji/CrosslingualIEIR.pdf>>, pp. 7.
Wang et al, “Translating Unknown Cross-Lingual Queries in Digital Libraries Using a Web-based Approach,” Proceedings of the 2004 Joint ACM/IEEE Conference on Digital Libraries (JCDL'04), 2004, pp. 108-116.
Wen et al, “Query Clustering Using User Logs,” ACM Transactions Information Systems, vol. 20, No. 1, Jan. 2002, pp. 59-81.
Diligenti et al., “A Unified Probabilistic Framework for Web Page Scoring Systems”, IEEE Transaction on Knowledge and Data Engineering, Jan. 2004, vol. 16, No. 1, pp. 4-16.

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