Combine Constituent and Dependency Parsing via Reranking

This paper presents a reranking approach to combining constituent and dependency parsing, aimed at improving parsing performance on both sides. Most previous combination methods rely on complicated joint decoding to integrate graph- and transition-based dependency models. Instead, our approach makes use of a high-performance probabilistic context free grammar (PCFG) model to output k-best candidate constituent trees, and then a dependency parsing model to rerank the trees by their scores from both models, so as to get the most probable parse. Experimental results show that this reranking approach achieves the highest accuracy of constituent and dependency parsing on Chinese treebank (CTB5.1) and a comparable performance to the state of the art on English treebank (WSJ).

[1]  Fernando Pereira,et al.  Non-Projective Dependency Parsing using Spanning Tree Algorithms , 2005, HLT.

[2]  Michael Collins,et al.  Discriminative Reranking for Natural Language Parsing , 2000, CL.

[3]  Jan Hajic,et al.  The Prague Dependency Treebank , 2003 .

[4]  Haizhou Li,et al.  K-Best Combination of Syntactic Parsers , 2009, EMNLP.

[5]  Yuji Matsumoto,et al.  Statistical Dependency Analysis with Support Vector Machines , 2003, IWPT.

[6]  R. Lathe.  Phd by thesis , 1988, Nature.

[7]  Hitoshi Isahara,et al.  Japanese Dependency Structure Analysis Based on Maximum Entropy Models , 1999, EACL.

[8]  Fernando Pereira,et al.  Online Learning of Approximate Dependency Parsing Algorithms , 2006, EACL.

[9]  Xavier Carreras,et al.  Experiments with a Higher-Order Projective Dependency Parser , 2007, EMNLP.

[10]  Liang Huang,et al.  Forest Reranking: Discriminative Parsing with Non-Local Features , 2008, ACL.

[11]  Kevin Knight,et al.  Combining Constituent Parsers , 2009, NAACL.

[12]  Mary P. Harper,et al.  Self-Training with Products of Latent Variable Grammars , 2010, EMNLP.

[13]  Qun Liu,et al.  Dependency Parsing and Projection Based on Word-Pair Classification , 2010, ACL.

[14]  Michael Collins,et al.  Three Generative, Lexicalised Models for Statistical Parsing , 1997, ACL.

[15]  Michael Collins,et al.  Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms , 2002, EMNLP.

[16]  Keith Hall,et al.  K-best Spanning Tree Parsing , 2007, ACL.

[17]  Daniel M. Bikel,et al.  Intricacies of Collins’ Parsing Model , 2004, CL.

[18]  James Henderson,et al.  Discriminative Training of a Neural Network Statistical Parser , 2004, ACL.

[19]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.

[20]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[21]  Yuji Matsumoto,et al.  Japanese Dependency Structure Analysis Based on Support Vector Machines , 2000, EMNLP.

[22]  Xavier Carreras,et al.  Simple Semi-supervised Dependency Parsing , 2008, ACL.

[23]  Dan Klein,et al.  Improved Inference for Unlexicalized Parsing , 2007, NAACL.

[24]  R. Bellman.  Dynamic programming. , 1957, Science.

[25]  Helmut Schmid,et al.  Features for Phrase-Structure Reranking from Dependency Parses , 2011, IWPT.

[26]  Xiao Chen,et al.  Discriminative constituent parsing with localized features , 2012 .

[27]  Xiao Chen,et al.  Higher-order Constituent Parsing and Parser Combination , 2012, ACL.

[28]  Eugene Charniak,et al.  A Maximum-Entropy-Inspired Parser , 2000, ANLP.

[29]  Rens Bod.  An efficient implementation of a new DOP model , 2003, EACL.

[30]  Michael Collins,et al.  Efficient Third-Order Dependency Parsers , 2010, ACL.

[31]  Slav Petrov,et al.  Products of Random Latent Variable Grammars , 2010, NAACL.

[32]  Eric P. Xing,et al.  Stacking Dependency Parsers , 2008, EMNLP.

[33]  Nathan Green,et al.  Hybrid Combination of Constituency and Dependency Trees into an Ensemble Dependency Parser , 2012 .

[34]  Eugene Charniak,et al.  Coarse-to-Fine n-Best Parsing and MaxEnt Discriminative Reranking , 2005, ACL.

[35]  Koby Crammer,et al.  Online Large-Margin Training of Dependency Parsers , 2005, ACL.

[36]  Dan Klein,et al.  Fast Exact Inference with a Factored Model for Natural Language Parsing , 2002, NIPS.

[37]  Beatrice Santorini,et al.  Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.

[38]  Joakim Nivre,et al.  Integrating Graph-Based and Transition-Based Dependency Parsers , 2008, ACL.

[39]  Stephen Clark,et al.  A Tale of Two Parsers: Investigating and Combining Graph-based and Transition-based Dependency Parsing , 2008, EMNLP.

[40]  M. V. Rossum,et al.  In Neural Computation , 2022 .

[41]  Xavier Carreras,et al.  TAG, Dynamic Programming, and the Perceptron for Efficient, Feature-Rich Parsing , 2008, CoNLL.

[42]  Xavier Carreras,et al.  An Empirical Study of Semi-supervised Structured Conditional Models for Dependency Parsing , 2009, EMNLP.

[43]  Dan Klein,et al.  Two Languages are Better than One (for Syntactic Parsing) , 2008, EMNLP.