NonSequeToR
Financiado
Non sequence models for tokenization replacement
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...
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.
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Duración del proyecto: 76 meses
Fecha Inicio: 2017-05-10
Fecha Fin: 2023-09-30
Fecha Fin: 2023-09-30
Línea de financiación: concedida
El organismo H2020 notifico la concesión del proyecto el día 2023-09-30
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2016-ADG:
ERC Advanced Grant
Cerrada
hace 8 años
Presupuesto
El presupuesto total del proyecto asciende a
3M€
Líder del proyecto
LUDWIGMAXIMILIANSUNIVERSITAET MUENCHEN
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5
169.15K€ |
Participante