nips nips2003 nips2003-147 nips2003-147-reference knowledge-graph by maker-knowledge-mining
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Author: Xavier Carreras, Lluís Màrquez
Abstract: This work presents an architecture based on perceptrons to recognize phrase structures, and an online learning algorithm to train the perceptrons together and dependently. The recognition strategy applies learning in two layers: a filtering layer, which reduces the search space by identifying plausible phrase candidates, and a ranking layer, which recursively builds the optimal phrase structure. We provide a recognition-based feedback rule which reflects to each local function its committed errors from a global point of view, and allows to train them together online as perceptrons. Experimentation on a syntactic parsing problem, the recognition of clause hierarchies, improves state-of-the-art results and evinces the advantages of our global training method over optimizing each function locally and independently. 1
[1] E. F. Tjong Kim Sang and S. Buchholz. Introduction to the CoNLL-2000 Shared Task: Chunking. In Proc. of CoNLL-2000 and LLL-2000, 2000.
[2] Erik F. Tjong Kim Sang and Herv´ D´ jean. Introduction to the CoNLL-2001 Shared Task: e e Clause Identification. In Proc. of CoNLL-2001, 2001.
[3] A. Ratnaparkhi. Learning to Parse Natural Language with Maximum-Entropy Models. Machine Learning, 34(1):151–175, 1999.
[4] V. Punyakanok and D. Roth. The Use of Classifiers in Sequential Inference. In Advances in Neural Information Processing Systems 13 (NIPS’00), 2001.
[5] T. Kudo and Y. Matsumoto. Chunking with Support Vector Machines . In Proc. of 2nd Conference of the North American Chapter of the Association for Computational Linguistics, 2001.
[6] X. Carreras, L. M` rquez, V. Punyakanok, and D. Roth. Learning and Inference for Clause a Identification. In Proceedings of the 14th ECML, Helsinki, Finland, 2002.
[7] T. Kudo and Y. Matsumoto. Japanese Dependency Analyisis using Cascaded Chunking . In Proc. of CoNLL-2002, 2002.
[8] K. Crammer and Y. Singer. A Family of Additive Online Algorithms for Category Ranking. Journal of Machine Learning Research, 3:1025–1058, 2003.
[9] S. Har-Peled, D. Roth, and D. Zimak. Constraint Classification for Multiclass Classification and Ranking. In Advances in Neural Information Processing Systems 15 (NIPS’02), 2003.
[10] M. Collins. Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms. In Proceedings of the EMNLP’02, 2002.
[11] Y. Freund and R. E. Schapire. Large Margin Classification Using the Perceptron Algorithm. Machine Learning, 37(3):277–296, 1999.