A Unified Framework for Scope Learning via Simplified Shallow Semantic Parsing

This paper approaches the scope learning problem via simplified shallow semantic parsing. This is done by regarding the cue as the predicate and mapping its scope into several constituents as the arguments of the cue. Evaluation on the BioScope corpus shows that the structural information plays a critical role in capturing the relationship between a cue and its dominated arguments. It also shows that our parsing approach significantly outperforms the state-of-the-art chunking ones. Although our parsing approach is only evaluated on negation and speculation scope learning here, it is portable to other kinds of scope learning.

[1]  János Csirik,et al.  The BioScope corpus: biomedical texts annotated for uncertainty, negation and their scopes , 2008, BMC Bioinformatics.

[2]  Nianwen Xue,et al.  Calibrating Features for Semantic Role Labeling , 2004, EMNLP.

[3]  Hai Zhao,et al.  Improving Nominal SRL in Chinese Language with Verbal SRL Information and Automatic Predicate Recognition , 2009, EMNLP.

[4]  Roser Morante,et al.  A Metalearning Approach to Processing the Scope of Negation , 2009, CoNLL.

[5]  György Szarvas,et al.  Hedge Classification in Biomedical Texts with a Weakly Supervised Selection of Keywords , 2008, ACL.

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

[7]  Wendy W. Chapman,et al.  A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries , 2001, J. Biomed. Informatics.

[8]  Stephan Oepen,et al.  Syntactic Scope Resolution in Uncertainty Analysis , 2010, COLING.

[9]  János Csirik,et al.  The CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text , 2010, CoNLL Shared Task.

[10]  Ilya M. Goldin,et al.  Learning to Detect Negation with ‘Not’ in Medical Texts , 2003 .

[11]  Nigel Collier,et al.  The GENIA project: corpus-based knowledge acquisition and information extraction from genome research papers , 1999, EACL.

[12]  Ted Briscoe,et al.  Weakly Supervised Learning for Hedge Classification in Scientific Literature , 2007, ACL.

[13]  Hwee Tou Ng,et al.  Semantic Role Labeling of NomBank: A Maximum Entropy Approach , 2006, EMNLP.

[14]  Dragomir R. Radev,et al.  Detecting Speculations and their Scopes in Scientific Text , 2009, EMNLP.

[15]  Dan Klein,et al.  Improved Inference for Unlexicalized Parsing , 2007, NAACL.

[16]  Xavier Carreras,et al.  Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling , 2005, CoNLL.

[17]  Dan Roth,et al.  The Necessity of Syntactic Parsing for Semantic Role Labeling , 2005, IJCAI.

[18]  Roser Morante,et al.  Learning the Scope of Negation in Biomedical Texts , 2008, EMNLP.

[19]  Padmini Srinivasan,et al.  The Language of Bioscience: Facts, Speculations, and Statements In Between , 2004, HLT-NAACL 2004.

[20]  Roser Morante,et al.  Learning the Scope of Hedge Cues in Biomedical Texts , 2009, BioNLP@HLT-NAACL.

[21]  Peter D. Turney.  Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews , 2002, ACL.

[22]  Jun'ichi Tsujii,et al.  Syntax Annotation for the GENIA Corpus , 2005, IJCNLP.

[23]  Hwee Tou Ng,et al.  An Empirical Evaluation of Knowledge Sources and Learning Algorithms for Word Sense Disambiguation , 2002, EMNLP.

[24]  János Csirik,et al.  The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts , 2008, BioNLP.

[25]  Nianwen Xue,et al.  Labeling Chinese Predicates with Semantic Roles , 2008, CL.