Objective
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.
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: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciences computer and information sciences artificial intelligence machine learning supervised learning
- natural sciences computer and information sciences data science natural language processing
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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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H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
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.
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.
ERC-STG - Starting Grant
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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-2015-STG
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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.
EH8 9YL Edinburgh
United Kingdom
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.