Objetivo Deep neural networks (DNNs) have become a critical tool in natural language processing (NLP) for a wide variety of language technologies, from syntax to semantics to pragmatics. In particular, in the field of natural language inference (NLI), DNNs have become the de-facto model, providing significantly better results than previous paradigms. Their power lies in their ability to embed complex language ambiguities in high dimensional spaces coupled with non-linear compositional transformations learned to directly optimize task-specific objective functions. We propose to adapt Deep NLI techniques to the biomedical domain, specifically investigating question answering, information extraction and synthesis. The biomedical domain presents many key challenges and a critical impact that standard NLI challenges do not posses. First, while standard NLI data sets requires a system to model basic world knowledge (e.g. that ‘soccer’ is a ‘sport’), they do not presume a rich domain knowledge encoded in various and often heterogeneous resources such as scientific articles, textbooks and structured databases. Second, while standard NLI data sets presume that the answer/inference is encoded in a single utterance, the ability to reason and extract information from biomedical domains often requires information synthesis from multiple utterances, paragraphs, and even documents. Finally, whereas standard NLI is a broad challenge aimed at testing whether computers can make general inferences in language, biomedical texts are a grounded and impactful domain where progress in automated reasoning will directly impact the efficacy of researchers, physicians, publishers and policy makers. Ámbito científico natural sciencescomputer and information sciencesdatabasesnatural sciencescomputer and information sciencesdata sciencenatural language processingnatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Palabras clave Biomedical information extraction question answering natural language inference deep learning Programa(s) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Tema(s) MSCA-IF-2016 - Individual Fellowships Convocatoria de propuestas H2020-MSCA-IF-2016 Consulte otros proyectos de esta convocatoria Régimen de financiación MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF) Coordinador ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS - RESEARCH CENTER Aportación neta de la UEn € 82 326,60 Dirección KEFALLINIAS STREET 46 11251 Athens Grecia Ver en el mapa Región Αττική Aττική Κεντρικός Τομέας Αθηνών Tipo de actividad Higher or Secondary Education Establishments Enlaces Contactar con la organización Opens in new window Participación en los programas de I+D de la UE Opens in new window Red de colaboración de HORIZON Opens in new window Coste total € 82 326,60