학술논문
MasakhaNER: Named Entity Recognition for African Languages
Document Type
Working Paper
Author
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; Azime, Israel Abebe; Muhammad, Shamsuddeen; Emezue, Chris Chinenye; Nakatumba-Nabende, Joyce; Ogayo, Perez; Aremu, Anuoluwapo; Gitau, Catherine; Mbaye, Derguene; Alabi, Jesujoba; Yimam, Seid Muhie; 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; Bateesa, Tobius Saul; 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; DIOP, Thierno Ibrahima; Diallo, Abdoulaye; Akinfaderin, Adewale; Marengereke, Tendai; Osei, Salomey
Source
Subject
Language
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.
Comment: Accepted to TACL 2021, pre-MIT Press publication version
Comment: Accepted to TACL 2021, pre-MIT Press publication version