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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| Veröffentlicht in: | Transactions of the Association for Computational Linguistics 2021-06 |
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| creator | 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 |
| description | 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 |
| doi_str_mv | 10.1162/tacl |
| format | Article |
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| ispartof | Transactions of the Association for Computational Linguistics, 2021-06 |
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| language | eng |
| recordid | cdi_hal_primary_oai_HAL_hal_03350962v1 |
| source | DOAJ Directory of Open Access Journals; EZB Electronic Journals Library; ProQuest Central |
| subjects | Computation and Language Computer Science |
| title | MasakhaNER: Named entity recognition for African languages |
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