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MasakhaNER: Named Entity Recognition for African Languages

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MasakhaNER: Named Entity Recognition for African Languages. / Adelani, David Ifeoluwa; Chukwuneke, Chiamaka.
In: arXiv, 22.03.2021.

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@article{af7df2f644e742d3ab5f99d4caef9451,
title = "MasakhaNER: Named Entity Recognition for African Languages",
abstract = "We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders. We detail characteristics of the languages to help researchers understand the challenges that these languages pose for NER. We analyze our datasets and conduct an extensive empirical evaluation of state-of-the-art methods across both supervised and transfer learning settings. We release the data, code, and models in order to inspire future research on African NLP.",
author = "Adelani, {David Ifeoluwa} and Chiamaka Chukwuneke",
note = "Accepted at the AfricaNLP Workshop @EACL 2021",
year = "2021",
month = mar,
day = "22",
language = "English",
journal = "arXiv",
issn = "2331-8422",

}

RIS

TY - JOUR

T1 - MasakhaNER

T2 - Named Entity Recognition for African Languages

AU - Adelani, David Ifeoluwa

AU - Chukwuneke, Chiamaka

N1 - Accepted at the AfricaNLP Workshop @EACL 2021

PY - 2021/3/22

Y1 - 2021/3/22

N2 - We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders. We detail characteristics of the languages to help researchers understand the challenges that these languages pose for NER. We analyze our datasets and conduct an extensive empirical evaluation of state-of-the-art methods across both supervised and transfer learning settings. We release the data, code, and models in order to inspire future research on African NLP.

AB - We take a step towards addressing the under-representation of the African continent in NLP research by creating the first large publicly available high-quality dataset for named entity recognition (NER) in ten African languages, bringing together a variety of stakeholders. We detail characteristics of the languages to help researchers understand the challenges that these languages pose for NER. We analyze our datasets and conduct an extensive empirical evaluation of state-of-the-art methods across both supervised and transfer learning settings. We release the data, code, and models in order to inspire future research on African NLP.

M3 - Journal article

JO - arXiv

JF - arXiv

SN - 2331-8422

ER -