Biomedical Information Synthesis with Deep Natural Language Inference

Información del proyecto

DNLIBiomed

Identificador del acuerdo de subvención: 751722

DOI

10.3030/751722

Proyecto finalizado el 28 Junio 2017

Fecha de la firma de la CE 8 Mayo 2017

Fecha de inicio 1 Octubre 2017

Fecha de finalización 30 Septiembre 2018

Financiado con arreglo a

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coste total

€ 82 326,60

Aportación de la UE

€ 82 326,60

Coordinado por

ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS - RESEARCH CENTER
Greece

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 (EuroSciVoc)

• ciencias naturales › informática y ciencias de la información › base de datos
• ciencias naturales › informática y ciencias de la información › ciencia de datos › procesamiento del lenguaje natural
• ciencias naturales › informática y ciencias de la información › inteligencia artificial › inteligencia computacional

Palabras clave

• Biomedical information extraction
• question answering
• natural language inference
• deep learning

Programa(s)

• H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions PROGRAMA PRINCIPAL
  
• H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
  

Tema(s)

• MSCA-IF-2016 - Individual Fellowships
  

Régimen de financiación

MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

Convocatoria de propuestas

H2020-MSCA-IF-2016

Coordinador

ATHENS UNIVERSITY OF ECONOMICS AND BUSINESS - RESEARCH CENTER

• Aportación neta de la UEn: € 82 326,60
• Dirección: KEFALLINIAS STREET 45 ; 112 57 ATHINA ; Grecia
• Región: Αττική › Aττική › Κεντρικός Τομέας Αθηνών
• Tipo de actividad: Higher or Secondary Education Establishments
• Coste total: € 82 326,60