Objectif 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 Champ scientifique natural sciencescomputer and information sciencesdata sciencenatural language processing Programme(s) FP7-PEOPLE - Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) Thème(s) FP7-PEOPLE-2013-CIG - Marie-Curie Action: "Career Integration Grants" Appel à propositions FP7-PEOPLE-2013-CIG Voir d’autres projets de cet appel Régime de financement MC-CIG - Support for training and career development of researcher (CIG) Coordinateur UNIVERSITY COLLEGE LONDON Contribution de l’UE € 100 000,00 Adresse GOWER STREET WC1E 6BT London Royaume-Uni Voir sur la carte Région London Inner London — West Camden and City of London Type d’activité Higher or Secondary Education Establishments Contact administratif Giles Machell (Mr.) Liens Contacter l’organisation Opens in new window Site web Opens in new window Coût total Aucune donnée
