Oyerinde, Samuel
The African Stopwords project: curating stopwords for African languages
Emezue, Chris, Nigatu, Hellina, Thinwa, Cynthia, Zhou, Helper, Muhammad, Shamsuddeen, Louis, Lerato, Abdulmumin, Idris, Oyerinde, Samuel, Ajibade, Benjamin, Samuel, Olanrewaju, Joshua, Oviawe, Onwuegbuzia, Emeka, Emezue, Handel, Ige, Ifeoluwatayo A., Tonja, Atnafu Lambebo, Chukwuneke, Chiamaka, Dossou, Bonaventure F. P., Etori, Naome A., Emmanuel, Mbonu Chinedu, Yousuf, Oreen, Aina, Kaosarat, David, Davis
Stopwords are fundamental in Natural Language Processing (NLP) techniques for information retrieval. One of the common tasks in preprocessing of text data is the removal of stopwords. Currently, while high-resource languages like English benefit from the availability of several stopwords, low-resource languages, such as those found in the African continent, have none that are standardized and available for use in NLP packages. Stopwords in the context of African languages are understudied and can reveal information about the crossover between languages. The African Stopwords project aims to study and curate stopwords for African languages. When analysing text data and building various NLP models, stopwords might not add much value to the meaning of the document (Singh, 2019) depending on the NLP task (like text classification).
MasakhaNER: Named Entity Recognition for African Languages
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
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.