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TENACITY

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Tensors and Neural Networks for Computational Creativity

Creativity in language is ubiquitous. It is abundantly present in work with an explicit creative intention - such as literary novels or poems - but weighty doses of creativity also pervade everyday language use. We believe that a... ver más

31/12/2028

KU Leuven

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

KATHOLIEKE UNIVERSITEIT LEUVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2023-06-19

ERC-2022-COG: ERC CONSOLIDATOR GRANTS Scope:Objectives

I+D

Cerrada hace 2 años

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1 Participantes

KU Leuven

1.99M€ | Lider

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Duración del proyecto: 66 meses Fecha Inicio: 2023-06-19
Fecha Fin: 2028-12-31

Líder del proyecto

KATHOLIEKE UNIVERSITEIT LEUVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Descripción del proyecto Creativity in language is ubiquitous. It is abundantly present in work with an explicit creative intention - such as literary novels or poems - but weighty doses of creativity also pervade everyday language use. We believe that a computational model of creativity that focuses on language will shed light on the enigmatic processes and interactions that come into play when we humans express ourselves in creative ways. Moreover, natural language generation systems - in order to produce realistic utterances - need to be endowed with a certain capacity for creativity. The main goal of this research project is to develop unsupervised models of language that exhibit creativity. In order to do so, we propose an integrated approach that combines a number of important and innovative techniques. First of all, we rely on constructs from linear algebra called tensors in order to express language content according to different parameters. Using tensors, we are able to induce latent semantics from multi-way co-occurrences of textual content, which can subsequently be used for the generation of creative expressions. Secondly, we rely on advanced machine learning techniques, notably neural networks. Neural network techniques have recently shown impressive performance in a number of natural language processing tasks. Yet, these techniques are mainly mimicking human language production, and thus are showing little creativity in language generation; by adapting neural network approaches in various ways, as well as integrating them with our tensor-based approach, we expect to develop algorithms that are able to grasp the meaning of textual content in a more profound and elaborate way, and at the same time are able to express it with creative intent. The project has the potential for groundbreaking results, not only because it would deepen our understanding of creativity, but also because of practical applications within the field of natural language processing.

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