BioNLP Shared Task 2011 - Bacteria Gene Interactions and Renaming

Document Type : Proceedings Paper Conference Date : JUN 23-24, 2011 Conference Location : Portland, ORInternational audienceWe present two related tasks of the BioNLP Shared Tasks 2011: Bacteria Gene Renaming (Rename) and Bacteria Gene Interactions (GI). We detail the objectives, the corpus specification, the evaluation metrics, and we summarize the participants' results. Both issued from PubMed scientific literature abstracts, the Rename task aims at extracting gene name synonyms, and the GI task aims at extracting genic interaction events, mainly about gene transcriptional regulations in bacteria

Thesaurus Enrichment via Coordination Extraction

International audienceWe advance a method of thesaurus enrichment, based on the extraction of coordinations in a domain-related corpus. Our hypothesis is that there is a semantic homogeneity between the conjuncts located in a coordination. We conducted an experiment that allowed us to evaluate the effectiveness of our method. This experiment aims to enrich the concept hierarchy of a French agricultural thesaurus named French Crop Usage (FCU), thanks to the texts of the Plant Health Bulletins (PHB). The FCU thesaurus is published on the Web using the SKOS model

BioNLP Shared Task - The Bacteria Track

Background: We 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. Results: Three 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. Conclusions: The 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

AlvisAE: a collaborative Web text annotation editor for knowledge acquisition

AlvisAE is a text annotation editor aimed at knowledge acquisition projects. An expressive annotation data model allows AlvisAE to support various knowledge acquisition tasks like construction gold standard corpus, ontology population and assisted reading. Collaboration is achieved through a workflow of tasks that emulates common practices (e.g. automatic pre-annotation, adjudication). It is implemented as a Web application requiring no installation by the end-user, thus facilitating the participation of domain experts. AlvisAE is used in several knowledge acquisition projects in the domains of biology and crop science.

Information Extraction

2nd revised editio