Bare-Bones Dependency Parsing - A Case for Occam's Razor?

If all we want from a syntactic parser is a dependency tree, what do we gain by first computing a different representation such as a phrase structure tree? The principle of parsimony suggests that a simpler model should be preferred over a more complex model, all other things being equal, and the simplest model is arguably one that maps a sentence directly to a dependency tree ‐ a bare-bones dependency parser. In this paper, I characterize the parsing problem faced by such a system, survey the major parsing techniques currently in use, and begin to examine whether the simpler model can in fact rival the performance of more complex systems. Although the empirical evidence is still limited, I conclude that bare-bones dependency parsers fare well in terms of parsing accuracy and often excel in terms of efficiency.

[1]  Mark Steedman,et al.  Unbounded Dependency Recovery for Parser Evaluation , 2009, EMNLP.

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

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

[4]  Hiroshi Maruyama,et al.  Structural Disambiguation with Constraint Propagation , 1990, ACL.

[5]  D. G. Hays Dependency Theory: A Formalism and Some Observations , 1964 .

[6]  Daniel Zeman,et al.  Improving Parsing Accuracy by Combining Diverse Dependency Parsers , 2005, IWPT.

[7]  Mark Johnson,et al.  Parsing the Wall Street Journal using a Lexical-Functional Grammar and Discriminative Estimation Techniques , 2002, ACL.

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

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

[10]  Michael Collins,et al.  Head-Driven Statistical Models for Natural Language Parsing , 2003, CL.

[11]  Christopher D. Manning,et al.  Generating Typed Dependency Parses from Phrase Structure Parses , 2006, LREC.

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

[13]  James R. Curran,et al.  Parsing the WSJ Using CCG and Log-Linear Models , 2004, ACL.

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

[15]  Joakim Nivre,et al.  MaltParser: A Data-Driven Parser-Generator for Dependency Parsing , 2006, LREC.

[16]  Joakim Nivre,et al.  Non-Projective Dependency Parsing in Expected Linear Time , 2009, ACL.

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

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

[19]  Ruken Cakici,et al.  Multi-lingual Dependency Parsing with Incremental Integer Linear Programming , 2006, CoNLL.

[20]  Eric P. Xing,et al.  Concise Integer Linear Programming Formulations for Dependency Parsing , 2009, ACL.

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

[22]  Daniel Jurafsky,et al.  Parsing to Stanford Dependencies: Trade-offs between Speed and Accuracy , 2010, LREC.

[23]  Joakim Nivre,et al.  An Efficient Algorithm for Projective Dependency Parsing , 2003, IWPT.

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

[25]  Jörg Tiedemann,et al.  Question Answering for Dutch using Dependency Relations , 2005, CLEF.

[26]  Giuseppe Attardi,et al.  Experiments with a Multilanguage Non-Projective Dependency Parser , 2006, CoNLL.

[27]  Keith Hall,et al.  Corrective Modeling for Non-Projective Dependency Parsing , 2005, IWPT.

[28]  Aron Culotta,et al.  Dependency Tree Kernels for Relation Extraction , 2004, ACL.

[29]  Joakim Nivre,et al.  Benchmarking of Statistical Dependency Parsers for French , 2010, COLING.

[30]  Jun'ichi Tsujii,et al.  Probabilistic Disambiguation Models for Wide-Coverage HPSG Parsing , 2005, ACL.

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

[32]  Martha Palmer,et al.  Synchronous Dependency Insertion Grammars: A Grammar Formalism for Syntax Based Statistical MT , 2004 .

[33]  Alexander M. Rush,et al.  Dual Decomposition for Parsing with Non-Projective Head Automata , 2010, EMNLP.

[34]  Dan Klein,et al.  Learning Accurate, Compact, and Interpretable Tree Annotation , 2006, ACL.

[35]  Kenji Sagae,et al.  Dynamic Programming for Linear-Time Incremental Parsing , 2010, ACL.

[36]  Wolfgang Menzel,et al.  Decision Procedures for Dependency Parsing Using Graded Constraints , 1998 .

[37]  Jun'ichi Tsujii,et al.  Shift-Reduce Dependency DAG Parsing , 2008, COLING.

[38]  Jason Eisner,et al.  Three New Probabilistic Models for Dependency Parsing: An Exploration , 1996, COLING.

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

[40]  Alon Lavie,et al.  Parser Combination by Reparsing , 2006, NAACL.

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

[42]  Joakim Nivre,et al.  Evaluation of Dependency Parsers on Unbounded Dependencies , 2010, COLING.