Mining Rules for Rewriting States in a Transition-based Dependency Parser for English

Methods for mining graph sequences have recently attracted considerable interest from researchers in the data-mining field. A graph sequence is one of the data structures that represent changing networks. The objective of graph sequence mining is to enumerate common changing patterns appearing more frequently than a given threshold from graph sequences. Syntactic dependency analysis has been recognized as a basic process in natural language processing. In a transition-based parser for dependency analysis, a transition sequence can be represented by a graph sequence where each graph, vertex, and edge respectively correspond to a state, word, and dependency. In this paper, we propose a method for mining rules for rewriting states reaching incorrect final states to states reaching the correct final state, and propose a dependency parser that uses rewriting rules. The proposed parser is comparable to conventional dependency parsers in terms of computational complexity.

[1]  George Karypis,et al.  Algorithms for mining the evolution of conserved relational states in dynamic networks , 2011, 2011 IEEE 11th International Conference on Data Mining.

[2]  Takashi Washio,et al.  A Fast Method to Mine Frequent Subsequences from Graph Sequence Data , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[3]  Peter A. Flach,et al.  Evaluation Measures for Multi-class Subgroup Discovery , 2009, ECML/PKDD.

[4]  Yuji Matsumoto,et al.  Japanese Dependency Analysis using Cascaded Chunking , 2002, CoNLL.

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

[6]  Yuji Matsumoto,et al.  Head-driven Transition-based Parsing with Top-down Prediction , 2012, ACL.

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

[8]  Yuji Matsumoto,et al.  Japanese Dependency Parsing Using a Tournament Model , 2008, COLING.

[9]  Takashi Washio,et al.  Efficient Graph Sequence Mining Using Reverse Search , 2011, IEICE Trans. Inf. Syst..

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

[11]  Erkki Mäkinen.  On the Subtree Isomorphism Problem for Ordered Trees , 1989, Inf. Process. Lett..

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

[13]  Kumiko Tanaka-Ishii,et al.  Tree-Based Deterministic Dependency Parsing — An Application to Nivre’s Method — , 2010, ACL.

[14]  Yuan Ding,et al.  Automatic Learning of Parallel Dependency Treelet Pairs , 2004, IJCNLP.

[15]  Yuji Matsumoto,et al.  Third-order Variational Reranking on Packed-Shared Dependency Forests , 2011, EMNLP.

[16]  Joakim Nivre,et al.  Algorithms for Deterministic Incremental Dependency Parsing , 2008, CL.

[17]  Joakim Nivre,et al.  Transition-based Dependency Parsing with Rich Non-local Features , 2011, ACL.

[18]  Aristides Gionis,et al.  Mining Graph Evolution Rules , 2009, ECML/PKDD.

[19]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[20]  Oi Yee Kwong,et al.  Natural Language Processing - IJCNLP 2004, First International Joint Conference, Hainan Island, China, March 22-24, 2004, Revised Selected Papers , 2005, IJCNLP.

[21]  Noah A. Smith,et al.  Dependency Parsing , 2009, Encyclopedia of Artificial Intelligence.

[22]  Taku Kudo,et al.  Boosting-based Parse Reranking with Subtree Features , 2005, ACL.

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

[24]  Brian Roark,et al.  Incremental Parsing with the Perceptron Algorithm , 2004, ACL.

[25]  Yoav Goldberg,et al.  An Efficient Algorithm for Easy-First Non-Directional Dependency Parsing , 2010, NAACL.

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

[27]  Umeshwar Dayal,et al.  PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth , 2001, ICDE 2001.

[28]  Giuseppe Attardi,et al.  Tree Revision Learning for Dependency Parsing , 2007, NAACL.

[29]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[30]  Hans-Peter Kriegel,et al.  Pattern Mining in Frequent Dynamic Subgraphs , 2006, Sixth International Conference on Data Mining (ICDM'06).