Zorana Ratkovic

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Bacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is(More)
This paper presents the Bacteria Biotope task of the BioNLP Shared Task 2013, which follows BioNLP-ST-11. The Bacteria Biotope task aims to extract the location of bacteria from scientific web pages and to characterize these locations with respect to the OntoBiotope ontology. Bacteria locations are crucial knowledge in biology for phenotype studies. The(More)
We present the two Bacteria Track tasks of BioNLP 2013 Shared Task (ST): Gene Regulation Network (GRN) and Bacteria Biotope (BB). These tasks were previously introduced in the 2011 BioNLP-ST Bacteria Track as Bacteria Gene Interaction (BI) and Bacteria Biotope (BB). The Bacteria Track was motivated by a need to develop specific BioNLP tools for fine-grained(More)
This paper describes the system of the INRA Bibliome research group applied to the Bacteria Biotope (BB) task of the BioNLP 2011 shared tasks. Bacteria, geographical locations and host entities were processed by a pattern-based approach and domain lexical resources. For the extraction of environment locations, we propose a framework based on semantic(More)
This paper presents a linguistic-based approach to term extraction from corpora in the biomedical domain. The method is based on an analysis of terms and their context that verify linguistic constraints. It focuses on participles and prepositional complements. The purpose of our approach is to obtain terms that are relevant for knowledge acquisition(More)
Questions are not asked in isolation. Their context, viz. the preceding interactions, might be of help to understand them and retrieve the correct answer. Previous research in Interactive Question Answering showed that context fusion has a big potential to improve the performance of answer retrieval. In this paper, we study how much context, and what(More)
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