Text-level Discourse Dependency Parsing

Previous researches on Text-level discourse parsing mainly made use of constituency structure to parse the whole document into one discourse tree. In this paper, we present the limitations of constituency based discourse parsing and first propose to use dependency structure to directly represent the relations between elementary discourse units (EDUs). The state-of-the-art dependency parsing techniques, the Eisner algorithm and maximum spanning tree (MST) algorithm, are adopted to parse an optimal discourse dependency tree based on the arcfactored model and the large-margin learning techniques. Experiments show that our discourse dependency parsers achieve a competitive performance on text-level discourse parsing.

[1]  Jason Baldridge,et al.  Probabilistic Head-Driven Parsing for Discourse Structure , 2005, CoNLL.

[2]  Danushka Bollegala,et al.  A Semi-Supervised Approach to Improve Classification of Infrequent Discourse Relations Using Feature Vector Extension , 2010, EMNLP.

[3]  Bonnie L. Webber,et al.  D-LTAG: extending lexicalized TAG to discourse , 2004, Cogn. Sci..

[4]  Mitsuru Ishizuka,et al.  HILDA: A Discourse Parser Using Support Vector Machine Classification , 2010, Dialogue Discourse.

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

[6]  William C. Mann,et al.  Rhetorical Structure Theory: Toward a functional theory of text organization , 1988 .

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

[8]  Michael Elhadad.  Book Review: Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper , 2010, CL.

[9]  Koby Crammer,et al.  Ultraconservative Online Algorithms for Multiclass Problems , 2001, J. Mach. Learn. Res..

[10]  David Reitter,et al.  Simple Signals for Complex Rhetorics: On Rhetorical Analysis with Rich-Feature Support Vector Models , 2003, LDV Forum.

[11]  Kenji Sagae,et al.  Analysis of Discourse Structure with Syntactic Dependencies and Data-Driven Shift-Reduce Parsing , 2009, IWPT.

[12]  Ani Nenkova,et al.  Automatic sense prediction for implicit discourse relations in text , 2009, ACL.

[13]  Ben Taskar,et al.  Max-Margin Markov Networks , 2003, NIPS.

[14]  Jian Su,et al.  Kernel Based Discourse Relation Recognition with Temporal Ordering Information , 2010, ACL.

[15]  Barbara Di Eugenio,et al.  An effective Discourse Parser that uses Rich Linguistic Information , 2009, NAACL.

[16]  Christian R. Huyck,et al.  Generating discourse structures for written texts , 2004, COLING 2004.

[17]  Hwee Tou Ng,et al.  Recognizing Implicit Discourse Relations in the Penn Discourse Treebank , 2009, EMNLP.

[18]  Livio Robaldo,et al.  The Penn Discourse TreeBank 2.0. , 2008, LREC.

[19]  Wiebke Wagner,et al.  Steven Bird, Ewan Klein and Edward Loper: Natural Language Processing with Python, Analyzing Text with the Natural Language Toolkit , 2010, Lang. Resour. Evaluation.

[20]  Daniel Marcu,et al.  Sentence Level Discourse Parsing using Syntactic and Lexical Information , 2003, NAACL.

[21]  Livio Robaldo,et al.  The Penn Discourse Treebank 2.0 Annotation Manual , 2007 .

[22]  James Pustejovsky,et al.  Classification of Discourse Coherence Relations: An Exploratory Study using Multiple Knowledge Sources , 2006, SIGDIAL Workshop.

[23]  Shafiq R. Joty,et al.  A Novel Discriminative Framework for Sentence-Level Discourse Analysis , 2012, EMNLP.

[24]  Graeme Hirst,et al.  Text-level Discourse Parsing with Rich Linguistic Features , 2012, ACL.

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

[26]  Daniel Marcu,et al.  Building a Discourse-Tagged Corpus in the Framework of Rhetorical Structure Theory , 2001, SIGDIAL Workshop.