Jointly Extracting Japanese Predicate-Argument Relation with Markov Logic

This paper describes a new Markov Logic approach for Japanese Predicate-Argument (PA) relation extraction. Most previous work built separated classifiers corresponding to each case role and independently identified the PA relations, neglecting dependencies (constraints) between two or more PA relations. We propose a method which collectively extracts PA relations by optimizing all argument candidates in a sentence. Our method can jointly consider dependency between multiple PA relations and find the most probable combination of predicates and their arguments in a sentence. In addition, our model involves new constraints to avoid considering inappropriate candidates for arguments and identify correct PA relations effectively. Compared to the state-of-the-art, our method achieves competitive results without largescale data.

[1]  Pedro M. Domingos,et al.  Joint Unsupervised Coreference Resolution with Markov Logic , 2008, EMNLP.

[2]  Yuji Matsumoto,et al.  A Structured Model for Joint Learning of Argument Roles and Predicate Senses , 2010, ACL.

[3]  Iván V. Meza,et al.  Jointly Identifying Predicates, Arguments and Senses using Markov Logic , 2009, NAACL.

[4]  Masaaki Nagata,et al.  A Japanese Predicate Argument Structure Analysis using Decision Lists , 2008, EMNLP.

[5]  J. Clarke,et al.  Global inference for sentence compression : an integer linear programming approach , 2008, J. Artif. Intell. Res..

[6]  Pedro M. Domingos,et al.  Joint Inference in Information Extraction , 2007, AAAI.

[7]  Manabu Okumura,et al.  Corpus-Based Analysis of Japanese Relative Clause Constructions , 2005, IJCNLP.

[8]  Thomas Lukasiewicz,et al.  Probabilistic Logic Programming , 1998, ECAI.

[9]  Mirella Lapata,et al.  Discourse Constraints for Document Compression , 2010, CL.

[10]  Xing Shi,et al.  Using First-Order Logic to Compress Sentences , 2012, AAAI.

[11]  Andrew McCallum,et al.  Introduction to Statistical Relational Learning , 2007 .

[12]  Kôiti Hasida,et al.  Construction of a Japanese Relevance-tagged Corpus , 2002, LREC.

[13]  Yuji Matsumoto,et al.  Exploiting Syntactic Patterns as Clues in Zero-Anaphora Resolution , 2006, ACL.

[14]  Daniel Gildea,et al.  The Proposition Bank: An Annotated Corpus of Semantic Roles , 2005, CL.

[15]  Iván V. Meza,et al.  Multilingual Semantic Role Labelling with Markov Logic , 2009, CoNLL Shared Task.

[16]  Tomoko Izumi,et al.  Discriminative Approach to Predicate-Argument Structure Analysis with Zero-Anaphora Resolution , 2009, ACL.

[17]  Ben Taskar,et al.  Discriminative Probabilistic Models for Relational Data , 2002, UAI.

[18]  Christopher D. Manning,et al.  A Global Joint Model for Semantic Role Labeling , 2008, CL.

[19]  Yuji Matsumoto,et al.  Capturing Salience with a Trainable Cache Model for Zero-anaphora Resolution , 2009, ACL.

[20]  Richard Johansson,et al.  The CoNLL-2009 Shared Task: Syntactic and Semantic Dependencies in Multiple Languages , 2009, CoNLL Shared Task.

[21]  Collin F. Baker,et al.  Building a Large Lexical Databank Which Provides Deep Semantics , 2001, PACLIC.

[22]  Pedro M. Domingos,et al.  Entity Resolution with Markov Logic , 2006, Sixth International Conference on Data Mining (ICDM'06).

[23]  Ben Taskar,et al.  Probabilistic Relational Models , 2014, Encyclopedia of Social Network Analysis and Mining.

[24]  Richard Johansson,et al.  The CoNLL 2008 Shared Task on Joint Parsing of Syntactic and Semantic Dependencies , 2008, CoNLL.

[25]  Matthew Richardson,et al.  Markov logic networks , 2006, Machine Learning.

[26]  Yuji Matsumoto,et al.  Annotating a Japanese Text Corpus with Predicate-Argument and Coreference Relations , 2007, LAW@ACL.