Summary
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.
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.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/740516 |
| Start date: | 01-10-2017 |
| End date: | 30-09-2023 |
| Total budget - Public funding: | 2 500 000,00 Euro - 2 500 000,00 Euro |
Cordis data
Original description
Natural language processing (NLP) is concerned withcomputer-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.
Status
CLOSEDCall topic
ERC-2016-ADGUpdate Date
27-04-2024
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