Integration of Static Relations to Enhance Event Extraction from Text

As research on biomedical text mining is shifting focus from simple binary relations to more expressive event representations, extraction performance drops due to the increase in complexity. Recently introduced data sets specifically targeting static relations between named entities and domain terms have been suggested to enable a better representation of the biological processes underlying annotated events and opportunities for addressing their complexity. In this paper, we present the first study of integrating these static relations with event data with the aim of enhancing event extraction performance. While obtaining promising results, we will argue that an event extraction framework will benefit most from this new data when taking intrinsic differences between various event types into account.

[1]  Jun'ichi Tsujii,et al.  Event Extraction with Complex Event Classification Using Rich Features , 2010, J. Bioinform. Comput. Biol..

[2]  Sampo Pyysalo,et al.  Static Relations: a Piece in the Biomedical Information Extraction Puzzle , 2009, BioNLP@HLT-NAACL.

[3]  Jun'ichi Tsujii,et al.  GENIA corpus - a semantically annotated corpus for bio-textmining , 2003, ISMB.

[4]  Sampo Pyysalo,et al.  A Re-Evaluation of Biomedical Named Entity-Term Relations , 2010, J. Bioinform. Comput. Biol..

[5]  Yue Wang,et al.  Incorporating GENETAG-style annotation to GENIA corpus , 2009, BioNLP@HLT-NAACL.

[6]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[7]  Preslav Nakov,et al.  SemEval-2007 Task 04: Classification of Semantic Relations between Nominals , 2007, Fourth International Workshop on Semantic Evaluations (SemEval-2007).

[8]  Jun'ichi Tsujii,et al.  A Rich Feature Vector for Protein-Protein Interaction Extraction from Multiple Corpora , 2009, EMNLP.

[9]  Jun'ichi Tsujii,et al.  Corpus annotation for mining biomedical events from literature , 2008, BMC Bioinformatics.

[10]  Yvan Saeys,et al.  Analyzing text in search of bio-molecular events: a high-precision machine learning framework , 2009, BioNLP@HLT-NAACL.

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

[12]  Claire Nédellec,et al.  Learning Language in Logic - Genic Interaction Extraction Challenge , 2005 .

[13]  Sampo Pyysalo,et al.  Overview of BioNLP’09 Shared Task on Event Extraction , 2009, BioNLP@HLT-NAACL.

[14]  Dan Klein,et al.  Accurate Unlexicalized Parsing , 2003, ACL.

[15]  Jari Björne,et al.  BioInfer: a corpus for information extraction in the biomedical domain , 2007, BMC Bioinformatics.