Improving graph-based random walks for complex question answering using syntactic, shallow semantic and extended string subsequence kernels

Information Processing and Management

Volumn 47, Issue 6, 2011, Pages 843-855

  Chali, Yllias ^a   Hasan, Sadid A ^a   Joty, Shafiq R ^b  

^a UNIVERSITY OF LETHBRIDGE   (Canada)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Complex question answering; Extended string subsequence kernel; Graph based method; Shallow semantic kernel; Syntactic kernel

Indexed keywords

COMPLEX QUESTIONS; EXTENDED STRING SUBSEQUENCE KERNEL; GRAPH-BASED; GRAPH-BASED METHODS; KERNEL FUNCTION; MULTI-DOCUMENT SUMMARIZATION; MULTIPLE DOCUMENTS; RANDOM WALK; RANDOM-WALK METHOD; RELATIVE IMPORTANCE; SEMANTIC INFORMATION; SHALLOW SEMANTIC KERNEL; SIMILARITY MEASURE;

NATURAL LANGUAGE PROCESSING SYSTEMS; RANDOM PROCESSES; SYNTACTICS;

SEMANTICS;

EID: 80052262254     PISSN: 03064573     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ipm.2010.10.002     Document Type: Article

References (29)

1. Amigo, E.; Gonzalo, J.; Peinado, V.; Peinado, A.; & Verdejo, F. (2004). An empirical study of information synthesis tasks. In: Proceedings of the 42nd annual meeting of the association for computational linguistics (pp. 207-214). Barcelona, Spain.
2. N. Cancedda, E. Gaussier, C. Goutte, and J.M. Renders Word sequence kernels Journal of Machine Learning Research 3 2003 1059 1082
3. Chali, Y.; & Joty, S. R. (2007). Word sense disambiguation using lexical cohesion. In: Proceedings of the 4th international conference on semantic evaluations (pp. 476-479). Prague: ACL.
4. Chali, Y.; & Joty, S. R. (2008). Improving the performance of the random walk model for answering complex questions. In: Proceedings of the 46th annual meeting of the association for computational linguistics: human language technologies. OH, USA: ACL.
5. Chali, Y.; & Joty, S. R. (2008). Exploiting syntactic and shallow semantic kernels to improve random walks for complex question answering. In: Proceedings of the 20th IEEE international conference on tools with artificial intelligence (ICTAI), IEEE, Dayton, OH, USA.
6. Charniak, E. (1999). A maximum-entropy-inspired parser. In: Technical Report CS-99-12. Brown University, Computer Science Department.
7. Collins, M.; & Duffy, N. (2001). Convolution kernels for natural language. In: Proceedings of Neural Information Processing Systems, Vancouver, Canada (pp. 625-632).
8. G. Erkan, and D.R. Radev LexRank: graph-based lexical centrality as salience in text summarization Journal of Artificial Intelligence Research 22 2004 457 479
9. C. Fellbaum WordNet - an electronic lexical database 1998 MIT Press Cambridge, MA
10. Goldstein, J.; Kantrowitz, M.; Mittal, V.; & Carbonell, J. (1999). Summarizing text documents: sentence selection and evaluation metrics. In: Proceedings of the 22nd international ACM conference on research and development in information retrieval, SIGIR, Berkeley, CA (pp. 121-128).
11. Hacioglu, K.; Pradhan, S.; Ward, W.; Martin, J.H.; & Jurafsky, D. (2003). Shallow semantic parsing using support vector machines. In: Technical Report TR-CSLR-2003-03. University of Colorado.
12. Hirao, T.; Suzuki, J.; Isozaki, H.; & Maeda, E. (2003). NTT's multiple document summarization system for DUC2003. In: Proceedings of the document understanding conference.
13. Hirao, T.; Suzuki, J.; Isozaki, H.; Maeda, E. (2004). Dependency-based sentence alignment for multiple document summarization. In: Proceedings of COLING 2004, COLING, Geneva, Switzerland (pp. 446-452).
14. Hovy, E.; Lin, C. Y.; Zhou, L.; & Fukumoto, J. (2006). Automated summarization evaluation with basic elements. In: Proceedings of the Fifth Conference on Language Resources and Evaluation, Genoa, Italy.
15. Kingsbury, P.; & Palmer, M. (2002). From treebank to propbank. In: Proceedings of the International Conference on Language Resources and Evaluation, Las Palmas, Spain.
16. Kouylekov, M.; & Magnini, B. (2005). Recognizing textual entailment with tree edit distance algorithms. In: Proceedings of the PASCAL Challenges Workshop: Recognising Textual Entailment Challenge.
17. Lin, C. Y. (2004). ROUGE: a package for automatic evaluation of summaries. In: Proceedings of workshop on text summarization branches out, post-conference workshop of association for computational linguistics, Barcelona, Spain (pp. 74-81).
18. Lin, C.Y.; & Hovy, E. (2003). Automatic evaluation of summaries using n-gram co-occurrence statistics. In: Proceedings of the annual meeting of the North American association for computational linguistics, Edmonton, Canada (pp. 150-156).
19. H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, and C. Watkins Text classification using string kernels Journal of Machine Learning Research 2 2002 419 444
20. MacCartney, B.; Grenager, T.; de Marneffe, M.; Cer, D.; & Manning, C. D. (2006). Learning to recognize features of valid textual entailments. In: proceedings of the human language technology conference of the North American chapter (pp. 41-48). New York, USA: ACL.
21. Mihalcea, R.; & Tarau, P. (2004). TextRank: bringing order into texts. In: Proceedings of the conference of empirical methods in natural language processing, Barcelona, Spain.
22. Moschitti, A.; & Basili, R. (2006). A tree kernel approach to question and answer classification in question answering systems. In: Proceedings of the 5th International Conference on Language Resources and Evaluation, Genoa, Italy.
23. Moschitti, A.; Quarteroni, S.; Basili, R.; Manandhar, S. (2007). Exploiting syntactic and shallow semantic kernels for question/answer classificaion. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (pp. 776-783). Prague, Czech Republic: ACL.
24. J. Otterbacher, G. Erkan, and D.R. Radev Using random walks for question-focused sentence retrieval Proceedings of human language technology conference and conference on empirical methods in natural language processing 2005 Vancouver Canada 915 922
25. Pingali, P.; Rahul, K.; & Varma, V. (2007). IIIT Hyderabad at DUC 2007. In: Proceedings of the document understanding conference, NIST, Rochester.
26. Punyakanok, V.; Roth, D.; & Yih, W. (2004). Mapping dependencies trees: an application to question answering. In: Proceedings of AI & Math, Florida, USA.
27. B. Scholkopf, and A. Smola Learning with kernels 2002 MIT Press Cambridge, MA
28. Shin, K.; & Kuboyama, T. (2008). A generalization of haussler's convolution kernel: mapping kernel. In: ICML (pp. 944-951).
29. Zhang, A.; & Lee, W. (2003). Question classification using support vector machines. In: Proceedings of the special interest group on information retrieval (pp. 26-32). Toronto, Canada: ACM.