Towards Event-based Discourse Analysis of Biomedical Text

Annotating biomedical text with discourse-level information is a well-studied topic. Several research efforts have annotated textual zones (e.g., sentences or clauses) with information about rhetorical status, whilst other efforts have linked and classified sets of text spans according to the type of discourse relation holding between them. A relatively new approach has involved annotating meta-knowledge (i.e., rhetorical intent and other types of information concerning interpretation) at the level of bio-events, which are structured representations of pieces of biomedical knowledge. In this paper, we report on the examination and comparison of transitions and patterns of event metaknowledge values that occur in both abstracts and full papers. Our analysis highlights a number of specific characteristics of event-level discourse patterns, as well as several noticeable differences between the types of patterns that occur in abstracts and full papers.

[1]  Sophia Ananiadou,et al.  Extracting semantically enriched events from biomedical literature , 2012, BMC Bioinformatics.

[2]  Sophia Ananiadou,et al.  Meta-Knowledge Annotation of Bio-Events , 2010, LREC.

[3]  Anita de Waard,et al.  Identifying Claimed Knowledge Updates in Biomedical Research Articles , 2012, ACL 2012.

[4]  Livio Robaldo,et al.  The Penn Discourse TreeBank 2.0. , 2008, LREC.

[5]  Jun'ichi Tsujii,et al.  New challenges for text mining: mapping between text and manually curated pathways , 2008, BMC Bioinformatics.

[6]  Sophia Ananiadou,et al.  Construction of an annotated corpus to support biomedical information extraction , 2009, BMC Bioinformatics.

[7]  Alexander A. Morgan,et al.  Evaluation of text data mining for database curation: lessons learned from the KDD Challenge Cup , 2003, ISMB.

[8]  Hong Yu,et al.  The biomedical discourse relation bank , 2011, BMC Bioinformatics.

[9]  John M. Swales,et al.  Genre Analysis: English in Academic and Research Settings , 1993 .

[10]  William C. Mann,et al.  Rhetorical Structure Theory: Toward a functional theory of text organization , 1988 .

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

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

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

[14]  Daniel Marcu,et al.  An Unsupervised Approach to Recognizing Discourse Relations , 2002, ACL.

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

[16]  Hagit Shatkay,et al.  New directions in biomedical text annotation: definitions, guidelines and corpus construction , 2006, BMC Bioinformatics.

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

[18]  Sampo Pyysalo,et al.  Event extraction across multiple levels of biological organization , 2012, Bioinform..

[19]  Sophia Ananiadou,et al.  Identification of Manner in Bio-Events , 2012, LREC.

[20]  Beatrice Santorini,et al.  Building a Large Annotated Corpus of English: The Penn Treebank , 1993, CL.

[21]  James Pustejovsky,et al.  FactBank: a corpus annotated with event factuality , 2009, Lang. Resour. Evaluation.

[22]  Sophia Ananiadou,et al.  Meta-Knowledge Annotation at the Event Level: Comparison between Abstracts and Full Papers , 2012, LREC 2012.

[23]  Jean Carletta,et al.  An annotation scheme for discourse-level argumentation in research articles , 1999, EACL.

[24]  Junichi Tsujii,et al.  Event extraction for systems biology by text mining the literature. , 2010, Trends in biotechnology.

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