JOINUS

Joint Inference with the Universal Schema

Coordinatore	UNIVERSITY COLLEGE LONDON    more  Organization address address: GOWER STREET city: LONDON postcode: WC1E 6BT contact info Titolo: Ms. Nome: Kamila Cognome: Kolasinska Email: send email Telefono: 442031000000 Fax: +44 20 78132849
Nazionalità Coordinatore	United Kingdom [UK]
Totale costo	100˙000 €
EC contributo	100˙000 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2013-CIG
Funding Scheme	MC-CIG
Anno di inizio	2013
Periodo (anno-mese-giorno)	2013-09-01   -   2017-08-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	UNIVERSITY COLLEGE LONDON  Organization address address: GOWER STREET city: LONDON postcode: WC1E 6BT contact info Titolo: Ms. Nome: Kamila Cognome: Kolasinska Email: send email Telefono: 442031000000 Fax: +44 20 78132849	UK (LONDON)	coordinator	100˙000.00

Mappa

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 Obiettivo del progetto (Objective)

'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'