Grant: Discovery Grants Program - Accelerator Supplements
Queen's University
$80,000.00
Exploring Better Distributed Representation and Composition Models for Semantics
EF 2019-20
Résumé
distributed representation,natural laguage processing,neural network models,recognizing textual entailment,representation learning,semantic compostion,semantics,sentiment analysis
Programme
Discovery Grants Program - Accelerator Supplements
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
522576-2018
Name nom
Zhu, Xiaodan
Department département
Electrical and Computer Engineering
Organization id
29
Institution établissement
Queen's University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2019
Competition year année de concours
2018
Award amount
80000
Program id
RGPAS
Program name en
Discovery Grants Program - Accelerator Supplements
Program name fr
Programme de subventions à la découverte - supplément d'accélération
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1559
Committee name en
DAS Selection Committee
Committee name fr
SAD Comité de sélection
Area of application code
801
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Computer software
Area of application fr
Logiciel
Research subject code
2800
Research subject group en
Artificial intelligence
Research subject group fr
Intelligence artificielle
Research subject en
Artificial intelligence (computer vision, use2603)
Research subject fr
Intelligence artificielle (vision artificielle, utiliser 2603)
Installment
inst:2 - 3
Partie
2
Nb partie
3
Application title
Exploring Better Distributed Representation and Composition Models for Semantics
Keyword
NSERC
Application summary
distributed representation,natural laguage processing,neural network models,recognizing textual entailment,representation learning,semantic compostion,semantics,sentiment analysis
