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Contenido archivado el 2024-06-18

Joint Inference with the Universal Schema

Objetivo

We are getting better and better in solving various subproblems in Natural Language Processing (NLP), such as parsing, coreference or relation extraction; however, once assembled into an end-to-end system of the traditional pipeline architecture, errors cascade and magnify. The principle goal of this project is to enable new generation of NLP applications in which information flow is bidirectional, and acquired downstream knowledge increases the robustness of upstream processing. Specifically, we want to investigate bidirectional flow in scenarios where downstream processing can acquire knowledge in very rich representations, and learn from massive amounts of unlabeled data. While this goal is motivated by the need for more accurate NLP, it also relates to the fundamental problem building artificial cognitive systems that adapt to their environment, seamlessly connect complex layers of abstraction and never stop learning. The work will have direct applications, for example, in extracting meta-data from media archives, biomedical text mining and information extraction from clinical texts

Ámbito científico (EuroSciVoc)

Programa(s)

Tema(s)

Convocatoria de propuestas

FP7-PEOPLE-2013-CIG
Consulte otros proyectos de esta convocatoria

Régimen de financiación

MC-CIG - Support for training and career development of researcher (CIG)

Coordinador

UNIVERSITY COLLEGE LONDON
Aportación de la UE
€ 100 000,00
Dirección
GOWER STREET
WC1E 6BT London
Reino Unido
Región
London Inner London — West Camden and City of London
Tipo de actividad
Higher or Secondary Education Establishments
Enlaces
Coste total
Sin datos