MasakhaNER: Named Entity Recognition for African Languages
Abstrak
AbstractWe take a step towards addressing the under- representation 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 state- of-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
Topik & Kata Kunci
Penulis (61)
David Ifeoluwa Adelani
Jade Abbott
Graham Neubig
Daniel D’souza
Julia Kreutzer
Constantine Lignos
Chester Palen-Michel
Happy Buzaaba
Shruti Rijhwani
Sebastian Ruder
Stephen Mayhew
Israel Abebe Azime
Shamsuddeen H. Muhammad
Chris Chinenye Emezue
Joyce Nakatumba-Nabende
Perez Ogayo
Aremu Anuoluwapo
Catherine Gitau
Derguene Mbaye
Jesujoba Alabi
Seid Muhie Yimam
Tajuddeen Rabiu Gwadabe
Ignatius Ezeani
Rubungo Andre Niyongabo
Jonathan Mukiibi
Verrah Otiende
Iroro Orife
Davis David
Samba Ngom
Tosin Adewumi
Paul Rayson
Mofetoluwa Adeyemi
Gerald Muriuki
Emmanuel Anebi
Chiamaka Chukwuneke
Nkiruka Odu
Eric Peter Wairagala
Samuel Oyerinde
Clemencia Siro
Tobius Saul Bateesa
Temilola Oloyede
Yvonne Wambui
Victor Akinode
Deborah Nabagereka
Maurice Katusiime
Ayodele Awokoya
Mouhamadane MBOUP
Dibora Gebreyohannes
Henok Tilaye
Kelechi Nwaike
Degaga Wolde
Abdoulaye Faye
Blessing Sibanda
Orevaoghene Ahia
Bonaventure F. P. Dossou
Kelechi Ogueji
Thierno Ibrahima DIOP
Abdoulaye Diallo
Adewale Akinfaderin
Tendai Marengereke
Salomey Osei
Akses Cepat
- Tahun Terbit
- 2021
- Sumber Database
- DOAJ
- DOI
- 10.1162/tacl_a_00416
- Akses
- Open Access ✓