Fast and effective kernels for relational learning from texts

ACM International Conference Proceeding Series

Volumn 227, Issue , 2007, Pages 649-656

  Moschitti, Alessandro ^a   Zanzotto, Fabio Massimo ^b  

^a UNIVERSITY OF TRENTO   (Italy)

^b UNIVERSITY OF ROME TOR VERGATA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; AUTOMATION; DYNAMIC PROGRAMMING; NATURAL LANGUAGE PROCESSING SYSTEMS; OPTIMIZATION; SUPPORT VECTOR MACHINES;

QUESTION ANSWERING; RELATIONAL LEARNING; SYNTACTIC KERNELS; TEXTUAL ENTAILMENT RECOGNITION;

TEXT PROCESSING;

EID: 34547970260     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1273496.1273578     Document Type: Conference Paper

References (18)

1. Bar Haim, R., Dagan, I., Dolan, B., Ferro, L., Giampiccolo, D., Magnini, B., & Szpektor, I. (2006). The II PASCAL RTE challenge. PASCAL Challenges Workshop. Venice, Italy.
2. Bikel, D., Schwartz, R., & Weischedel, R. (1999). An Algorithm that Learns What's in a Name. Machine Learning, Special Issue on Natural Language Learning.
3. Boughorbel, S., Tarel, J.-P., & Fleuret, F. (2004). Non-mercer kernel for SVM object recognition. Proceedings of BMVC 2004. London, England.
4. Charniak, E. (2000). A maximum-entropy-inspired parser. Proc. of the 1st NAACL. Seattle, Washington, USA.
5. Collins, M., & Duffy, N. (2002). New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron. Proceedings of ACL02. Morristown, NJ, USA.
6. Corley, C., & Mihalcea, R. (2005). Measuring the semantic similarity of texts. Proc. of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment. Ann Arbor, Michigan, USA.
7. Cumby, C., & Roth, D. (2003). Kernel methods for relational learning. Proceedings of ICML 2003. Washington, DC, USA.
8. Dagan, I., Glickman, O., & Magnini, B. (2005). The PASCAL RTE challenge. PASCAL Challenges Workshop. Southampton, U.K.
9. Getoor, L. (2005). Tutorial on statistical relational learning. ILP (p. 415).
10. Haasdonk, B. (2005). Feature space interpretation of SVMs with indefinite kernels. IEEE Trans Pattern Anal Mach Intell, 27.
11. Joachims, T. (1999). Making large-scale svm learning practical. Advances in Kernel Methods-Support Vector Learning. MIT Press.
12. Miller, G. A. (1995). WordNet: A lexical database for English. Communications of the ACM.
13. Moschitti, A. (2006). Efficient convolution kernels for dependency and constituent syntactic trees. Proceedings of ECML, Berlin, Germany.
14. Peñas, A., Rodrigo, A., Sama, V., & Verdejo, F. (2006). Overview of the answer validation exercise 2006. Working Notes for the CLEF 2006 Workshop. Alicante, Spain.
15. Ponte, J. M., & Croft, W. B. (1998). A language modeling approach to information retrieval. Proceedings of SIGIR '98. New York, NY, USA.
16. Voorhees, E. M. (2003). Overview of TREC 2003. TREC.
17. Zanzotto, F. M., & Moschitti, A. (2006). Automatic learning of textual entailments with cross-pair similarities. Proceedings of the 21st Coling and 44th ACL. Sydney, Australia.
18. Zelenko, D., Aone, C., & Richardella, A. (2003). Kernel methods for relation extraction. Journal of Machine Learning Research.