DISCOTEX

Distributional Compositional Semantics for Text Processing

 Coordinatore THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE 

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 Nazionalità Coordinatore United Kingdom [UK]
 Totale costo 1˙087˙930 €
 EC contributo 1˙087˙930 €
 Programma FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call ERC-2012-StG_20111012
 Funding Scheme ERC-SG
 Anno di inizio 2012
 Periodo (anno-mese-giorno) 2012-09-01   -   2017-08-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE

 Organization address address: The Old Schools, Trinity Lane
city: CAMBRIDGE
postcode: CB2 1TN

contact info
Titolo: Dr.
Nome: Stephen
Cognome: Clark
Email: send email
Telefono: -1223763660

UK (CAMBRIDGE) hostInstitution 1˙087˙930.00
2    THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE

 Organization address address: The Old Schools, Trinity Lane
city: CAMBRIDGE
postcode: CB2 1TN

contact info
Titolo: Ms.
Nome: Renata
Cognome: Schaeffer
Email: send email
Telefono: +44 1223 333543
Fax: +44 1223 332988

UK (CAMBRIDGE) hostInstitution 1˙087˙930.00

Mappa


 Word cloud

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

nlp    formal    science    words    meanings    linguistics    computer    compositional    language    semantics    idea    ai    model    natural    sentence    distributional    generation    philosophy    determined   

 Obiettivo del progetto (Objective)

'The notion of meaning is central to many areas of Computer Science, Artificial Intelligence (AI), Linguistics, Philosophy, and Cognitive Science. A formal account of the meaning of natural language utterances is crucial to AI, since an understanding of natural language is at the heart of much intelligent behaviour. More specifically, Natural Language Processing (NLP) --- the branch of AI concerned with the automatic processing, analysis and generation of text --- requires a model of meaning for many of its tasks and applications.

There have been two main approaches to modelling the meaning of language in NLP. The first, the ``compositional' approach, is based on classical ideas from Philosophy and Mathematical Logic, and includes formal accounts of how the meaning of a sentence can be determined from the relations of words in a sentence. The second, more recent approach focuses on the meanings of the words themselves. This is the ``distributional' approach to lexical semantics and is based on the idea that the meanings of words can be determined by considering the contexts in which words appear in text.

The ambitious idea in this proposal is to exploit the strengths of the two approaches, by developing a unified model of distributional and compositional semantics, and exploiting it for NLP tasks and applications. The aim is to make the following fundamental contributions:

1. advance the theoretical study of meaning in Linguistics, Computer Science and AI; 2. develop new meaning-sensitive approaches to NLP applications which can be robustly applied to naturally occurring text.

The claim is that language technology based on ``shallow' approaches is reaching its performance limit, and the next generation of language technology requires a more sophisticated, but robust, model of meaning, which this project will provide.'

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