Wide coverage natural language processing using kernel methods and neural networks for structured data

Pattern Recognition Letters

Volumn 26, Issue 12, 2005, Pages 1896-1906

  Menchetti, Sauro ^a   Costa, Fabrizio ^a   Frasconi, Paolo ^a   Pontil, Massimiliano ^b  

^a UNIVERSITY OF FLORENCE   (Italy)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Convolution kernels; Learning preferences; Natural language; Recursive neural networks

Indexed keywords

GRAPH THEORY; LEARNING ALGORITHMS; LOGIC PROGRAMMING; NEURAL NETWORKS; PATTERN RECOGNITION; VECTORS;

CLASSIFICATION REGRESSION; CONVOLUTION KERNELS; LEARNING PREFERENCES; RECURSIVE NEURAL NETWORKS;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 22944487701     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2005.03.011     Document Type: Conference Paper

References (19)

1. Bod, R., 2001. What is the minimal set of fragments that achieves maximal parse accuracy? In: Proceedings of ACL
2. W.W. Cohen, R.E. Schapire, and Y. Singer Learning to order things J. Artif. Intell. Res. 10 1999 243 270
3. Collins, M., Duffy, N., 2001. Convolution Kernels for natural language. In: Proc. Neural Information Processing Systems, NIPS 14
4. Collins, M., Duffy, N., 2002. New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron. In: Proc. of ACL
5. Costa, F., Frasconi, P., Lombardo, V., Sturt, P., Soda, G., submitted for publication. Ambiguity resolution analysis in incremental parsing of natural language. IEEE Transactions on Neural Network
6. F. Cuetos, and D. Mitchell Cross-linguistic differences in parsing: Restrictions on the use of the late closure strategy in Spanish Cognition 30 1 1988 73 105
7. P. Frasconi, M. Gori, and A. Sperduti A general framework for adaptative processing of data structure IEEE Trans. Neural Networks 9 5 1998 768 786
8. Y. Freund, and R.E. Schapire Large margin classification using the perceptron algorithm Mach. Learn. 37 3 1999 277 296
9. Goller, C., Kuechler, A., 1996. Learning task-dependent distributed structure-representations by back-propagation through structure. In: IEEE International Conference on Neural networks, pp. 347-352
10. Haussler, D., 1999. Convolution Kernels on Discrete Structures, Tech. Rep. UCSC-CLR-99-10, University of California at Santa Cruz
11. R. Herbrich, T. Graepel, and K. Obermayer Large margin rank boundaries for ordinal regression A. Smola P. Bartlett B. Schölkopf D. Schuurmans Advances in Large Margin Classifiers 2000 MIT Press
12. Jaakkola, T., Haussler, D., 1998. Exploiting generative models in discriminative classifiers. In: Proc. Neural Information Processing Systems, NIPS 10
13. Joachims, T., 2002. Evaluating retrieval performance using clickthrough data. In: Proceedings of the SIGIR Workshop on Mathematical/Formal Methods in Information Retrieval
14. C. Leslie, E. Eskin, and W.S. Noble The spectrum Kernel: A string Kernel for SVM protein classification Pacific Sympos. Biocomput. 2002 566 575
15. Lodhi, H., Shawe-Taylor, J., Cristianini, N., Watkins, C., 2000. Text classification using string Kernels. In: Proc. Neural Information Processing Systems, NIPS 13, pp. 563-569
16. Lombardo, V., Sturt, P., in press. Incrementality and lexicalism: A treebank study. In: Stevenson, S., Merlo, P. (Eds.), Lexical Representations in Sentence Processing. Computational Psycholingusitics Series, John Benjamins
17. M. Marcus, B. Santorini, and M. Marcinkiewicz Building a large annotated corpus of English: The Penn Treebank Computat. Linguist. 19 1993 313 330
18. D. Mitchell, F. Cuetos, M. Corley, and M. Brysbaert Exposure-based models of human parsing: Evidence for the use of coarse-grained (nonlexical) statistical records J. Psycholinguist. Res. 24 6 1995 469 488
19. P. Sturt, F. Costa, V. Lombardo, and P. Frasconi Learning first-pass structural attachment preferences with dynamic grammars and recursive neural networks Cognition 88 2 2003 133 169