Hola,
¿eres nuevo aquí?

Regístrate gratis y conecta tu empresa con financiación pública, partners y proyectos.

Tengo cuenta

Regístrate

Ver video

BroadSem

Financiado

Induction of Broad Coverage Semantic Parsers

In the last one or two decades, language technology has achieved a number of important successes, for example, producing functional machine translation systems and beating humans in quiz games. The key bottleneck which prevents fu... ver más

30/04/2022

UEDIN

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

THE UNIVERSITY OF EDINBURGH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 3 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2022-04-30

ERC-StG-2015: ERC Starting Grant

I+D

Cerrada hace 9 años

0% 100% 100%

3 Participantes

UEDIN

1.29M€ | Lider

GENIUS PHARMA AIE

UvA

167.24K€ | Participante

Conecta tu I+D

¿Tienes un proyecto y buscas un partner? Gracias a nuestro motor inteligente podemos recomendarte los mejores socios y ponerte en contacto con ellos. Te lo explicamos en este video

Proyectos interesantes

TIN2015-68955-REDT RED DE RECURSOS PARA TECNOLOGIAS DE LA LENGUA 35K€ Cerrado

TIN2015-72646-EXP GENERACION AUTOMATICA DE PROCESADORES SEMANTICOS 45K€ Cerrado

TIN2009-14715-C04-03 REPRESENTACION DEL CONOCIMIENTO SEMANTICO 55K€ Cerrado

TIN2012-38523-C02-01 TRADUCCION AUTOMATICA EN CONTEXTO Y AUMENTADA CON RECURSOS D... 122K€ Cerrado

FFI2017-82454-P GENERADOR MULTILINGUE DE ESTRUCTURAS ARGUMENTALES DEL SUSTAN... 36K€ Cerrado

FFI2010-14986 ONTOPEDIA: EXTRACCION AUTOMATICA DE INFORMACION ONTOLOGICA Y... 70K€ Cerrado

Duración del proyecto: 76 meses Fecha Inicio: 2015-12-16
Fecha Fin: 2022-04-30

Líder del proyecto

THE UNIVERSITY OF EDINBURGH No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto In the last one or two decades, language technology has achieved a number of important successes, for example, producing functional machine translation systems and beating humans in quiz games. The key bottleneck which prevents further progress in these and many other natural language processing (NLP) applications (e.g., text summarization, information retrieval, opinion mining, dialog and tutoring systems) is the lack of accurate methods for producing meaning representations of texts. Accurately predicting such meaning representations on an open domain with an automatic parser is a challenging and unsolved problem, primarily because of language variability and ambiguity. The reason for the unsatisfactory performance is reliance on supervised learning (learning from annotated resources), with the amounts of annotation required for accurate open-domain parsing exceeding what is practically feasible. Moreover, representations defined in these resources typically do not provide abstractions suitable for reasoning. In this project, we will induce semantic representations from large amounts of unannotated data (i.e. text which has not been labeled by humans) while guided by information contained in human-annotated data and other forms of linguistic knowledge. This will allow us to scale our approach to many domains and across languages. We will specialize meaning representations for reasoning by modeling relations (e.g., facts) appearing across sentences in texts (document-level modeling), across different texts, and across texts and knowledge bases. Learning to predict this linked data is closely related to learning to reason, including learning the notions of semantic equivalence and entailment. We will jointly induce semantic parsers (e.g., log-linear feature-rich models) and reasoning models (latent factor models) relying on this data, thus, ensuring that the semantic representations are informative for applications requiring reasoning.

Conecta tu I+D

Entra hoy

¿Olvidé mi contraseña?

Financiación

Empresas

CTIs/Universidades

Proyectos

Investigadores