Towards Creation of a Corpus for Argumentation Mining the Biomedical Genetics Research Literature

Argumentation mining involves automatically identifying the premises, conclusion, and type of each argument as well as relationships between pairs of arguments in a document. We describe our plan to create a corpus from the biomedical genetics research literature, annotated to support argumentation mining research. We discuss the argumentation elements to be annotated, theoretical challenges, and practical issues in creating such a corpus.

[1]  Nancy L. Green.  Analysis of Linguistic Features Associated with Point of View for Generating Stylistically Appropriate Text , 2006, Computing Attitude and Affect in Text.

[2]  Nigel Collier,et al.  Zone analysis in biology articles as a basis for information extraction , 2006, Int. J. Medical Informatics.

[3]  Ido Dagan,et al.  Recognizing textual entailment: Rational, evaluation and approaches , 2009, Natural Language Engineering.

[4]  Marc Moens,et al.  Articles Summarizing Scientific Articles: Experiments with Relevance and Rhetorical Status , 2002, CL.

[5]  Nancy Green,et al.  Analysis of communication of uncertainty in genetic counseling patient letters for design of a natural language generation system , 2010 .

[6]  Sophia Ananiadou,et al.  A three-way perspective on scientific discourse annotation for knowledge extraction , 2012, ACL 2012.

[7]  Patrick Saint-Dizier,et al.  Some Facets of Argument Mining for Opinion Analysis , 2012, COMMA.

[8]  Anita de Waard,et al.  Epistemic Modality and Knowledge Attribution in Scientific Discourse: A Taxonomy of Types and Overview of Features , 2012 .

[9]  Marie-Francine Moens,et al.  Argumentation mining , 2011, Artificial Intelligence and Law.

[10]  Sophia Ananiadou,et al.  Enriching a biomedical event corpus with meta-knowledge annotation , 2011, BMC Bioinformatics.

[11]  Marie-Francine Moens,et al.  Approaches to Text Mining Arguments from Legal Cases , 2010, Semantic Processing of Legal Texts.

[12]  Trevor J. M. Bench-Capon,et al.  Semi-Automated Argumentative Analysis of Online Product Reviews , 2012, COMMA.

[13]  Nancy Green,et al.  Representation of Argumentation in Text with Rhetorical Structure Theory , 2010 .

[14]  Akira Inagaki,et al.  A mutation in CABP2, expressed in cochlear hair cells, causes autosomal-recessive hearing impairment. , 2012, American journal of human genetics.

[15]  Ron Artstein,et al.  Survey Article: Inter-Coder Agreement for Computational Linguistics , 2008, CL.

[16]  Dietrich Rebholz-Schuhmann,et al.  Automatic recognition of conceptualization zones in scientific articles and two life science applications , 2012, Bioinform..

[17]  Analía Amandi,et al.  Building user argumentative models , 2010, Applied Intelligence.

[18]  Nancy Green,et al.  A Bayesian network coding scheme for annotating biomedical information presented to genetic counseling clients , 2005, J. Biomed. Informatics.

[19]  Serena Villata,et al.  Generating Abstract Arguments: A Natural Language Approach , 2012, COMMA.

[20]  Akira Shimazu,et al.  Learning Logical Structures of Paragraphs in Legal Articles , 2011, IJCNLP.

[21]  Anita de Waard,et al.  Epistemic Modality and Knowledge Attribution in Scientific Discourse: A Taxonomy of Types and Overview of Features , 2012, ACL 2012.

[22]  Simone Teufel,et al.  The Structure of Scientific Articles - Applications to Citation Indexing and Summarization , 2010, CSLI Studies in Computational Linguistics.

[23]  Chris Reed,et al.  Araucaria: Software for Argument Analysis, Diagramming and Representation , 2004, Int. J. Artif. Intell. Tools.

[24]  Marie-Francine Moens,et al.  Language Resources for Studying Argument , 2008, LREC.

[25]  Chris Reed,et al.  Argumentation Schemes , 2008 .

[26]  Nancy L. Green,et al.  Natural language generation of biomedical argumentation for lay audiences , 2011, Argument Comput..

[27]  Karin Baier,et al.  The Uses Of Argument , 2016 .