Information extraction from bibliography for Marker Assisted Selection in wheat

Improvement of most animal and plant species of agronomical interest in the near future has become an international stake because of the increasing demand for feeding a growing world population and to mitigate the reduction of the industrial resources. The recent advent of genomic tools contributed to improve the discovery of linkage between molecular markers and genes that are involved in the control of traits of agronomical interest such as grain number or disease resistance. This information is mostly published as scientific papers but rarely available in databases. Here, we present a method aiming at automatically extract this information from the scientific literature and relying on a knowledge model of the target information and on the WheatPhenotype ontology that we developed for this purpose. The information extraction results were evaluated and integrated into the on-line semantic search engine [i]AlvisIR WheatMarker.[/i

Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach

International audienceBackground: 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 rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field

Corpus-based extension of termino-ontology by linguistic analysis: a use case in biomedical event extraction

The automatic population of a termino-ontology is a difficult and challenging task. We propose a text-based ontology extension method that was experimented and evaluated on, for a semantic annota-tion task in the biomedical domain. It is based on the linguistic analysis of terms and their heads. The head-based method improves both the identification of rele-vant areas of a termino-ontology and the matching of the corpus terms within the-se areas

Corpus-based extension of termino-ontology by linguistic analysis: a use case in biomedical event extraction

National audienceThe automatic population of a termino-ontology is a difficult and challenging task. We propose a text-based ontology extension method that was experimented and evaluated on, for a semantic annota-tion task in the biomedical domain. It is based on the linguistic analysis of terms and their heads. The head-based method improves both the identification of rele-vant areas of a termino-ontology and the matching of the corpus terms within the-se areas

Improving term extraction with linguistic analysis in the biomedical domain

International audienceThis paper presents a linguistic-based approach to term extraction in the biomedical domain. The method is based on a linguistic analysis of constraints on terms and their context, focusing on participles and prepositional complements. The purpose of our approach is to obtain terms that are relevant for knowledge acquisition applications, such as the creation and enrichment of terminologies and ontologies. We report on the evaluations conducted following two complementary strategies, using a reference terminology and a manual validation. They were applied to two corpora of differing genre and domain, namely pharmacology patents and animal physiology scientific articles. Our work shows that the linguistic analysis-based developments significantly improve extraction results. The method is especially efficient when dealing with gerunds and "to" prepositional modifier

TyDI : un outil d’assistance à la construction de terminologie structurée

National audienc

Improving term extraction with linguistic analysis in the biomedical domain

This paper presents a linguistic-based approach to term extraction in the biomedical domain. The method is based on a linguistic analysis of constraints on terms and their context, focusing on participles and prepositional complements. The purpose of our approach is to obtain terms that are relevant for knowledge acquisition applications, such as the creation and enrichment of terminologies and ontologies. We report on the evaluations conducted following two complementary strategies, using a reference terminology and a manual validation. They were applied to two corpora of differing genre and domain, namely pharmacology patents and animal physiology scientific articles. Our work shows that the linguistic analysis-based developments significantly improve extraction results. The method is especially efficient when dealing with gerunds and "to" prepositional modifier

Improving term extraction with linguistic analysis in the biomedical domain

à ce jour 31/01/2014 cette parution est en " draft version "International audienceThis paper presents a linguistic-based approach to term extraction in the biomedical domain. The method is based on a linguistic analysis of constraints on terms and their context, focusing on participles and prepositional complements. The purpose of our approach is to obtain terms that are relevant for knowledge acquisition applications, such as the creation and enrichment of terminologies and ontologies. We report on the evaluations conducted following two complementary strategies, using a reference terminology and a manual validation. They were applied to two corpora of differing genre and domain, namely pharmacology patents and animal physiology scientific articles. Our work shows that the linguistic analysis-based developments significantly improve extraction results. The method is especially efficient when dealing with gerunds and "to" prepositional modifier

Building large lexicalized ontologies from text: a use case in automatic indexing of biotechnology patents

International audienceThis paper presents a tool, TyDI, and methods experimented in the building of a termino-ontology, i.e. a lexicalized ontology aimed at fine-grained indexation for semantic search applications. TyDI provides facilities for knowledge engineers and domain experts to efficiently collaborate to validate, organize and conceptualize corpus extracted terms. A use case on biotechnology patent search demonstrates TyDI’s potential