MasakhaNER: Named entity recognition for African languages

We take a step towards addressing the underrepresentation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristic...

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Published inTransactions of the Association for Computational Linguistics
Main Authors Adelani, David Ifeoluwa, Abbott, Jade, Neubig, Graham, d'Souza, Daniel, Kreutzer, Julia, Lignos, Constantine, Palen-Michel, Chester, Buzaaba, Happy, Rijhwani, Shruti, Ruder, Sebastian, Mayhew, Stephen, Abebe Azime, Israel, Muhammad, Shamsuddeen H, Chinenye Emezue, Chris, Nakatumba-Nabende, Joyce, Ogayo, Perez, Aremu, Anuoluwapo, Gitau, Catherine, Mbaye, Derguene, Alabi, Jesujoba, Yimam, Seid Muhie, Rabiu Gwadabe, Tajuddeen, Ezeani, Ignatius, Niyongabo, Rubungo Andre, Mukiibi, Jonathan, Otiende, Verrah, Orife, Iroro, David, Davis, Ngom, Samba, Adewumi, Tosin, Rayson, Paul, Adeyemi, Mofetoluwa, Muriuki, Gerald, Anebi, Emmanuel, Chukwuneke, Chiamaka, Odu, Nkiruka, Wairagala, Eric Peter, Oyerinde, Samuel, Siro, Clemencia, Saul Bateesa, Tobius, Oloyede, Temilola, Wambui, Yvonne, Akinode, Victor, Nabagereka, Deborah, Katusiime, Maurice, Awokoya, Ayodele, Mboup, Mouhamadane, Gebreyohannes, Dibora, Tilaye, Henok, Nwaike, Kelechi, Wolde, Degaga, Faye, Abdoulaye, Sibanda, Blessing, Ahia, Orevaoghene, Dossou, Bonaventure F P, Ogueji, Kelechi, Thierno, Ibrahima, Diallo, Abdoulaye, Akinfaderin, Adewale, Marengereke, Tendai, Osei, Salomey
Format Journal Article
LanguageEnglish
Published The MIT Press 14.06.2021
Subjects
Computation and Language
Computer Science
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Summary:We take a step towards addressing the underrepresentation of the African continent in NLP research by bringing together different stakeholders to create the first large, publicly available, high-quality dataset for named entity recognition (NER) in ten African languages. We detail the characteristics of these languages to help researchers and practitioners better understand the challenges they pose for NER tasks. We analyze our datasets and conduct an extensive empirical evaluation of stateof-the-art methods across both supervised and transfer learning settings. Finally, we release the data, code, and models to inspire future research on African NLP. 1
ISSN:2307-387X
DOI:10.1162/tacl