acl acl2010 acl2010-71 acl2010-71-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Min Zhang ; Hui Zhang ; Haizhou Li
Abstract: This paper proposes a convolution forest kernel to effectively explore rich structured features embedded in a packed parse forest. As opposed to the convolution tree kernel, the proposed forest kernel does not have to commit to a single best parse tree, is thus able to explore very large object spaces and much more structured features embedded in a forest. This makes the proposed kernel more robust against parsing errors and data sparseness issues than the convolution tree kernel. The paper presents the formal definition of convolution forest kernel and also illustrates the computing algorithm to fast compute the proposed convolution forest kernel. Experimental results on two NLP applications, relation extraction and semantic role labeling, show that the proposed forest kernel significantly outperforms the baseline of the convolution tree kernel. 1
ACE (2002-2006). The Automatic Content Extraction Projects. http://www.ldc.upenn.edu/Projects/ACE/ Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti and Alessandro Moschitti. 2006. Fast Online Kernel Learning for Trees. ICDM-2006 Fabio Aiolli, Giovanni Da San Martino, Alessandro Sperduti and Alessandro Moschitti. 2007. Efficient Kernel-based Learning for Trees. IEEE Symposium on Computational Intelligence and Data Mining (CIDM-2007) J. Baker. 1979. Trainable grammars for speech recognition. The 97th meeting of the Acoustical Society of America S. Billot and S. Lang. 1989. The structure of shared forest in ambiguous parsing. ACL-1989 Razvan Bunescu. 2008. Learning with Probabilistic Features for Improved Pipeline Models. EMNLP2008 X. Carreras and Lluıs Ma rquez. 2005. Introduction to the CoNLL-2005 shared task: SRL. CoNLL-2005 E. Charniak. 2001 . Immediate-head Parsing for Language Models. ACL-2001 E. Charniak and Mark Johnson. 2005. Corse-to-finegrained n-best parsing and discriminative reranking. ACL-2005 Wanxiang Che, Min Zhang, Ting Liu and Sheng Li. 2006. A hybrid convolution tree kernel for semantic role labeling. COLING-ACL-2006 (poster) WanXiang Che, Min Zhang, Aiti Aw, Chew Lim Tan, Ting Liu and Sheng Li. 2008. Using a Hybrid Convolution Tree Kernel for Semantic Role Labeling. ACM Transaction on Asian Language Information Processing M. Collins. 1999. Head-driven statistical models for natural language parsing. Ph.D. dissertation, Pennsylvania University M. Collins and N. Duffy. 2002. Convolution Kernels for Natural Language. NIPS-2002 Christopher Dyer, Smaranda Muresan and Philip Resnik. 2008. Generalizing Word Lattice Translation. ACL-HLT-2008 Jenny Rose Finkel, Christopher D. Manning and Andrew Y. Ng. 2006. Solving the Problem of Cascading Errors: Approximate Bayesian Inference for Linguistic Annotation Pipelines. EMNLP-2006 Y. Freund and R. E. Schapire. 1999. Large margin classification using the perceptron algorithm. Machine Learning, 37(3):277-296 D. Guldea. 2002. Probabilistic models of verbargument structure. COLING-2002 D. Haussler. 1999. Convolution Kernels on Discrete Structures. Technical Report UCS-CRL-99-10, University of California, Santa Cruz Liang Huang. 2008. Forest reranking: Discriminative parsing with non-local features. ACL-2008 Karim Lari and Steve J. Young. 1990. The estimation of stochastic context-free grammars using the inside-outside algorithm. Computer Speech and Language. 4(35–56) H. Kashima and T. Koyanagi. 2003. Kernels for SemiStructured Data. ICML-2003 Dan Klein and Christopher D. Manning. 2001. Parsing and Hypergraphs. IWPT-2001 T. Joachims. 1998. Text Categorization with Support Vecor Machine: learning with many relevant features. ECML-1998 Haitao Mi and Liang Huang. 2008. Forest-based Translation Rule Extraction. EMNLP-2008 Alessandro Moschitti. 2004. A Study on Convolution Kernels for Shallow Semantic Parsing. ACL-2004 Alessandro Moschitti. 2006. Syntactic kernels for natural language learning: the semantic role labeling case. HLT-NAACL-2006 (short paper) Martha Palmer, Dan Gildea and Paul Kingsbury. 2005. The proposition bank: An annotated corpus of semantic roles. Computational Linguistics. 3 1(1) F. Rosenblatt. 1962. Principles of Neurodynamics: Perceptrons and the theory of brain mechanisms. Spartan Books, Washington D.C. Masaru Tomita. 1987. An Efficient AugmentedContext-Free Parsing Algorithm. Computational Linguistics 13(1-2): 3 1-46 Vladimir N. Vapnik. Theory. Wiley 1998. Statistical Learning C. Watkins. 1999. Dynamic alignment kernels. In A. J. Smola, B. Sch¨olkopf, P. Bartlett, and D. Schuurmans (Eds.), Advances in kernel methods. MIT Press Nianwen Xue and Martha Palmer. 2004. Calibrating features for semantic role labeling. EMNLP-2004 Xiaofeng Yang, Jian Su and Chew Lim Tan. 2006. Kernel-Based Pronoun Resolution with Structured Syntactic Knowledge. COLING-ACL-2006 Dell Zhang and W. Lee. 2003. Question classification using support vector machines. SIGIR-2003 Hui Zhang, Min Zhang, Haizhou Li, Aiti Aw and Chew Lim Tan. 2009a. Forest-based Tree Sequence to String Translation Model. ACLIJCNLP-2009 Hui Zhang, Min Zhang, Haizhou Li and Chew Lim Tan. 2009b. Fast Translation Rule Matching for 884 Syntax-based EMNLP-2009 Statistical Machine Translation. Min Zhang, Jie Zhang, Jian Su and GuoDong Zhou. 2006. A Composite Kernel to Extract Relations between Entities with Both Flat and Structured Features. COLING-ACL-2006 Min Zhang, W. Che, A. Aw, C. Tan, G. Zhou, T. Liu and S. Li. 2007. A Grammar-driven Convolution Tree Kernel for Semantic Role Classification. ACL-2007 Min Zhang, Hongfei Jiang, Aiti Aw, Haizhou Li, Chew Lim Tan and Sheng Li. 2008. A Tree Sequence Alignment-based Tree-to-Tree Translation Model. ACL-2008 Min Zhang and Haizhou Li. 2009. Tree Kernel-based SVM with Structured Syntactic Knowledge for BTG-based Phrase Reordering. EMNLP-2009 885