Objective
Natural language processing (NLP) is concerned with
computer-based processing of natural language, with
applications such as human-machine interfaces and
information access. The capabilities of NLP are currently
severely limited compared to humans. NLP has high error
rates for languages that differ from English (e.g.
languages with higher morphological complexity like Czech)
and for text genres that are not well edited (or noisy) and
that are of high economic importance, e.g. social media
text.
NLP is based on machine learning, which requires as basis a
representation that reflects the underlying structure of the
domain, in this case the structure of language. But
representations currently used are symbol-based: text is
broken into surface forms by sequence models that implement
tokenization heuristics and treat each surface form as a
symbol or represent it as an embedding (a vector
representation) of that symbol. These heuristics are
arbitrary and error-prone, especially for non-English and
noisy text, resulting in poor performance.
Advances in deep learning now make it possible to take the
embedding idea and liberate it from the limitations of
symbolic tokenization. I have the interdisciplinary
expertise in computational linguistics, computer science and
deep learning required for this project and am thus in the
unique position to design a radically new robust and
powerful non-symbolic text representation that captures all
aspects of form and meaning that NLP needs for successful
processing.
By creating a text representation for NLP that is not
impeded by the limitations of symbol-based tokenization, the
foundations are laid to take NLP applications like
human-machine interaction, human-human communication
supported by machine translation and information access to
the next level.
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.
- humanities languages and literature linguistics
- natural sciences computer and information sciences data science natural language processing
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences computer and information sciences artificial intelligence heuristic programming
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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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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-ADG - Advanced 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-2016-ADG
See all projects funded under this callHost institution
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.
80539 MUNCHEN
Germany
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.