Project description
Developing computational models of linguistic creativity
Creativity in language is everywhere, not just in literature but also in everyday language use. A computational model of linguistic creativity can be useful for two reasons: it could provide insight into the processes of human creative expression, and it could enable natural language generation systems to produce realistic discourse. Funded by the European Research Council, the TENACITY project aims to develop autonomous models of linguistic creativity through a novel approach that combines tensors and neural networks. Standard neural network techniques are already performing well in some natural language-processing tasks, but they simply imitate human language production and lack creativity. TENACITY aspires to overcome this limitation by developing algorithms capable of grasping meaning from text and expressing it creatively.
Objective
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2022-COG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
3000 LEUVEN
Belgium
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.