Extraction of predicate-argument structure from sentence based on PICO frames

Identifying a logical relation between sentences and semantic role labelling requires a deeper knowledge of recognizing the relationship of various expressions. One method that can be used is by means of the extraction of predicate argument structure. This paper is purposely to describe a new automatic method for the extraction of Indonesian medical predicate-argument (P-A) structure analysis based upon PICO frame. Learning some relevant features, the method assigns some case roles (such as Problem/Population/Patient, Intervention, Compare/Control and Outcome) to the argument of the target predicate using the features of the words that are located closest to the target predicate. In this paper the illustration of their use in a pattern-based relation extraction component of PICO frame has been described. It is indicated from the test results that the use of the features with more semantic role categories in determining the P-A structure represents the respective results reaching at 89.35% for precision, 89.12% for recall and 89.98% for F1.

[1]  Yuji Matsumoto,et al.  Dual decomposition method for chinese predicate-argument structure analysis , 2011, 2011 7th International Conference on Natural Language Processing and Knowledge Engineering.

[2]  Kentaro Torisawa,et al.  Similarity Based Language Model Construction for Voice Activated Open-Domain Question Answering , 2011, IJCNLP.

[3]  Sanda M. Harabagiu,et al.  Using Predicate-Argument Structures for Information Extraction , 2003, ACL.

[4]  Shafiq R. Joty,et al.  Selecting Sentences for Answering Complex Questions , 2008, EMNLP.

[5]  Daniel Gildea,et al.  The Necessity of Parsing for Predicate Argument Recognition , 2002, ACL.

[6]  Alexander M. Rush,et al.  On Dual Decomposition and Linear Programming Relaxations for Natural Language Processing , 2010, EMNLP.

[7]  Josef Ruppenhofer,et al.  FrameNet II: Extended theory and practice , 2006 .

[8]  Yuji Matsumoto,et al.  Two-Phased Event Relation Acquisition: Coupling the Relation-Oriented and Argument-Oriented Approaches , 2008, COLING.

[9]  Naoki Abe,et al.  Feasible Learnability of Formal Grammars and The Theory of Natural Language Acquisition , 1988, COLING.

[10]  Razvan C. Bunescu,et al.  A Shortest Path Dependency Kernel for Relation Extraction , 2005, HLT.

[11]  Yuji Matsumoto,et al.  A Database of Relations between Predicate Argument Structures for Recognizing Textual Entailment and Contradiction , 2008, 2008 Second International Symposium on Universal Communication.

[12]  Yuji Matsumoto,et al.  Acquiring causal knowledge from text using the connective marker tame , 2005, TALIP.

[13]  Johan Eklund,et al.  Mixing and Blending Syntactic and Semantic Dependencies , 2008, CoNLL.

[14]  Patrick Pantel,et al.  VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations , 2004, EMNLP.

[15]  Tatsuya Kawahara,et al.  Language modeling for spoken dialogue system based on sentence transformation and filtering using predicate-argument structures , 2012, Proceedings of The 2012 Asia Pacific Signal and Information Processing Association Annual Summit and Conference.

[16]  Ralph Grishman,et al.  Automatic Acquisition of Domain Knowledge for Information Extraction , 2000, COLING.