Answer clustering and fusion in a user-interactive QA system

2006 2nd International Conference on Semantics Knowledge and Grid, SKG

Volumn , Issue , 2006, Pages 41-46

  Min, Feng ^a   Wenyin, Liu ^a   Chen, Wei ^a  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

NATURAL LANGUAGE PROCESSING SYSTEMS; SEMANTICS;

CLUSTERING APPROACH; DATA-SOURCES; ENABLING TECHNOLOGIES; FUSION METHODS; QA SYSTEM; SEMANTIC SIMILARITY; USER STUDY; USER-INTERACTIVE QUESTION ANSWERING;

USER INTERFACES;

EID: 44949178747     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SKG.2006.28     Document Type: Conference Paper

References (24)

1. BaiduZhidao. http://zhidao.baidu.com/.
2. CuteAid. http://www.cs.cityu.edu.hk/QA/.
3. Google Answer, http://answers.google.com/answers/.
4. Sina LAsk. http://iask.sina.com.cn/.
5. J. Allen. Natural language understanding (2nd ed.). Benjamin-Cummings Publishing Co., Inc., Redwood City, CA, USA, 1995.
6. A. Banerjee, I. S. Dhillon, J. Ghosh, and S. Sra. Clustering on the unit hypersphere using von mises-fisher distributions. Journal of Machine Learning Research, 6:1345-1382, 2005.
7. A. Banerjee and J. Langford. An objective evaluation criterion for clustering. In KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 515-520, Seattle, WA, USA, 2004. ACM Press.
8. W. Chen, Q. Zeng, and W. Liu. A user reputation model for a user-interactive question answering system. Accepted by SKG'06 workshop.
9. I. Dhillon and D. Modha. Concept decompositions for large sparse text data using clustering. Machine Learning, 42(1-2): 143-175, 2001.
10. S. Dumais, M. Banko, E. Brill, J. Lin, and A. Ng. Web question answering: is more always better? In SIGIR '02: Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pages 291-298, Tampere, Finland, 2002. ACM Press.
11. C. Fellbaum. WordNet: A lexical databse for English. MIT Press, Cambridge, MA, 1998.
12. V. Hatzivassiloglou, J. Klavans, and E. Eskin. Detecting text similarity over short passages: exploring linguistic feature combinations via machine learning. In EMNLP '99: Proceedings of empirical methods in natural language processing and very large corpora, MD, USA, 1999.
13. G. Karypis. CLUTO - A clustering toolkit. Technical report, Dept of Computer Science, University of Minnesota, 2002. http://www-users.cs.umn.edu/ karypis/cluto/.
14. T. Landauer, D. Lanam, and P. Foltz. How well can passage meaning be derived without using word order? a comparison of latent semantic analysis and humans. In Proceedings of 19th Annual Meeting of the Cognitive Science Society, pages 412-417, 1997.
15. Y. Li, D. McLean, Z. Bandar, J. O'Shea, and K. Crockett. Sentence similarity based on semantic nets and corpus statistics. IEEE Transactions on Knowledge and Data Engineering, 18(8):1138-1150, 2006.
16. C. Meadow, D. Kraft, and B. Boyce. Text Information Retrieval Systems. Academic Press, Inc., Orlando, FL, USA, 1999.
17. M. Rodriguez and M. Egenhofer. Determining semantic similarity among entity classes from different ontologies. IEEE Transactions on Knowledge and Data Engineering, 15:442-456, 2003.
18. M. Sahami and T. Heilman. A web-based kernel function for measuring the similarity of short text snippets. In Proceedings of the 15th International Conference on World Wide Web, pages 377-386, Edinburgh, Scotland, 2006. ACM Press.
19. A. Strehl and J. Ghosh. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3:583-617, 2003.
20. O. Zamir and O. Etzioni. Grouper: a dynamic clustering interface to web search results. Computer Networks, 31(11-16):1361-1374, 1999.
21. D. Zhang. Web based question answering with aggregation strategy. In Proceedings of the 6th Asia Pacific Web Conference (APWEB2004), Hangzhou, China, 2004.
22. S. Zhong and J. Ghosh. Generative model-based document clustering: a comparative study. Knowledge and Information Systems, 8(3):374-384, 2005.
23. H. Zhuge. China's e-science knowledge grid environment. IEEE Intelligent Systems, 19:13-17, 2004.
24. H. Zhuge. The knowledge grid. World Scientific Publishing Co., Singapore, 2004.