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In this paper, we describe the Machine Learning system, asium 1 , which learns Subcaterorization Frames of verbs and ontologies from the syntactic parsing of technical texts in natural language. The restrictions of selection in the subcategorization frames are lled by the ontology's concepts. Applications requiring such knowledge are crucial and numerous.(More)
The goal of the Genic Regulation Network task (GRN) is to extract a regulation network that links and integrates a variety of molecular interactions between genes and proteins of the well-studied model bacterium Bacillus subtilis. It is an extension of the BI task of BioNLP-ST'11. The corpus is composed of sentences selected from publicly available PubMed(More)
The BioNLP Shared Task 2013 is the third edition of the BioNLP Shared Task series that is a community-wide effort to address fine-grained, structural information extraction from biomedical literature. The BioNLP Shared Task 2013 was held from January to April 2013. Six main tasks were proposed. 38 final submissions were received, from 22 teams. The results(More)
This paper presents the Bacteria Biotope task as part of the BioNLP Shared Tasks 2011. The Bacteria Biotope task aims at extracting the location of bacteria from scientific Web pages. Bacteria location is a crucial knowledge in biology for phenotype studies. The paper details the corpus specification, the evaluation metrics, summarizes and discusses the(More)
This paper presents the SeeDev Task of the BioNLP Shared Task 2016. The purpose of the SeeDev Task is the extraction from scientific articles of the descriptions of genetic and molecular mechanisms involved in seed development of the model plant, Arabidopsis thaliana. The SeeDev task consists in the extraction of many different event types that involve a(More)
This paper describes Mo'K, a configurable workbench that supports the development of conceptual clustering methods for ontology building. Mo'K is intended to assist ontology developers in the exploratory process of defining the most suitable learning methods for a given task. To do so, it provides facilities for evaluation, comparison, characterization and(More)
This paper gives an overview of the Caderige project. This project involves teams from different areas (biology, machine learning, natural language processing) in order to develop high-level analysis tools for extracting structured information from biological bibliographical databases, especially Medline. The paper gives an overview of the approach and(More)
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(More)
In some domains, Information Extraction (IE) from texts requires syntactic and semantic parsing. This analysis is computationally expensive and IE is potentially noisy if it applies to the whole set of documents when the relevant information is sparse. A preprocessing phase that selects the fragments which are potentially relevant increases the efficiency(More)