Cardamom: Comparative Deep Models for Minority and Historical Languages

Theodorus Fransen

doi:http://hdl.handle.net/10379/16107

Cardamom: Comparative Deep Models for Minority and Historical Languages

Theodorus Fransen

Computer Science

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

This paper gives an overview of the Cardamom project, which aims to close the resource gap for minority and under-resourced languages by means of deep-learning-based natural language processing (NLP) and exploiting similarities of closely-related languages. The project further extends this idea to historical languages, which can be considered as closely related to their modern form, and as such aims to provide NLP through both space and time for languages that have been ignored by current approaches.

Original language	English (Ireland)
Title of host publication	Proceedings of the 1st International Conference on Language Technologies for All
Place of Publication	Paris, France
DOIs	https://doi.org/http://hdl.handle.net/10379/16107
Publication status	Published - 1 Dec 2019

Authors (Note for portal: view the doc link for the full list of authors)

Authors
McCrae, J; Fransen, T

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Cite this

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title = "Cardamom: Comparative Deep Models for Minority and Historical Languages",

abstract = "This paper gives an overview of the Cardamom project, which aims to close the resource gap for minority and under-resourced languages by means of deep-learning-based natural language processing (NLP) and exploiting similarities of closely-related languages. The project further extends this idea to historical languages, which can be considered as closely related to their modern form, and as such aims to provide NLP through both space and time for languages that have been ignored by current approaches.",

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AB - This paper gives an overview of the Cardamom project, which aims to close the resource gap for minority and under-resourced languages by means of deep-learning-based natural language processing (NLP) and exploiting similarities of closely-related languages. The project further extends this idea to historical languages, which can be considered as closely related to their modern form, and as such aims to provide NLP through both space and time for languages that have been ignored by current approaches.

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ER -

Cardamom: Comparative Deep Models for Minority and Historical Languages

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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Cite this