Paulin Melatagia Yonta ; Michael Franklin Mbouopda
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Named Entity Recognition in Low-resource Languages using Cross-lingual distributional word representation
arima:6439 -
Revue Africaine de Recherche en Informatique et Mathématiques Appliquées,
September 29, 2020,
Volume 33 - Special issue CRI 2019 - 2020-21
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https://doi.org/10.46298/arima.6439
Named Entity Recognition in Low-resource Languages using Cross-lingual distributional word representationArticle
Authors: Paulin Melatagia Yonta 1; Michael Franklin Mbouopda 1
Named Entity Recognition (NER) is a fundamental task in many NLP applications that seek to identify and classify expressions such as people, location, and organization names. Many NER systems have been developed, but the annotated data needed for good performances are not available for low-resource languages, such as Cameroonian languages. In this paper we exploit the low frequency of named entities in text to define a new suitable cross-lingual distributional representation for named entity recognition. We build the first Ewondo (a Bantu low-resource language of Cameroon) named entities recognizer by projecting named entity tags from English using our word representation. In terms of Recall, Precision and F-score, the obtained results show the effectiveness of the proposed distributional representation of words