BioNLP Shared Task - The Bacteria Track

BMC Bioinformatics

Proceedings

Maarten van de Guchte¹  Robert Bossy²  Claire Nédellec²  Julien Jourde²  Philippe Veber²  Philippe Bessières²  Erick Alphonse³  Alain-Pierre Manine³ 

[1] MICALIS, Institut National de la Recherche Agronomique, UMR1319 - F78352, Jouy-en-Josas, France;Mathématique Informatique et Génome, Institut National de la Recherche Agronomique, INRA UR1077 - F78352, Jouy-en-Josas, France;PredictiveDB - 16, rue Alexandre Parodi, F75010, Paris, France;

关键词: Inductive Logic Programming;    Entity Recognition;    Event Extraction;    Reference Event;    NCBI Taxonomy;   

DOI  :  10.1186/1471-2105-13-S11-S3

来源: Springer

【 摘 要 】

BackgroundWe present the BioNLP 2011 Shared Task Bacteria Track, the first Information Extraction challenge entirely dedicated to bacteria. It includes three tasks that cover different levels of biological knowledge. The Bacteria Gene Renaming supporting task is aimed at extracting gene renaming and gene name synonymy in PubMed abstracts. The Bacteria Gene Interaction is a gene/protein interaction extraction task from individual sentences. The interactions have been categorized into ten different sub-types, thus giving a detailed account of genetic regulations at the molecular level. Finally, the Bacteria Biotopes task focuses on the localization and environment of bacteria mentioned in textbook articles.We describe the process of creation for the three corpora, including document acquisition and manual annotation, as well as the metrics used to evaluate the participants' submissions.ResultsThree teams submitted to the Bacteria Gene Renaming task; the best team achieved an F-score of 87%. For the Bacteria Gene Interaction task, the only participant's score had reached a global F-score of 77%, although the system efficiency varies significantly from one sub-type to another. Three teams submitted to the Bacteria Biotopes task with very different approaches; the best team achieved an F-score of 45%. However, the detailed study of the participating systems efficiency reveals the strengths and weaknesses of each participating system.ConclusionsThe three tasks of the Bacteria Track offer participants a chance to address a wide range of issues in Information Extraction, including entity recognition, semantic typing and coreference resolution. We found commond trends in the most efficient systems: the systematic use of syntactic dependencies and machine learning. Nevertheless, the originality of the Bacteria Biotopes task encouraged the use of interesting novel methods and techniques, such as term compositionality, scopes wider than the sentence.

【 授权许可】

Unknown   
© Bossy et al; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.