JOINUS

Joint Inference with the Universal Schema

 Coordinatore 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

 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


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

nlp    flow    bidirectional    downstream    data    extraction   

 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'

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