Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Emezue, P."'
Autor:
Emezue, Chris
Structured prediction tasks, like machine translation, involve learning functions that map structured inputs to structured outputs. Recurrent Neural Networks (RNNs) have historically been a popular choice for such tasks, including in natural language
Externí odkaz:
http://arxiv.org/abs/2405.11819
Autor:
Emezue, Chris Chinenye, Okoh, Ifeoma, Mbonu, Chinedu, Chukwuneke, Chiamaka, Lal, Daisy, Ezeani, Ignatius, Rayson, Paul, Onwuzulike, Ijemma, Okeke, Chukwuma, Nweya, Gerald, Ogbonna, Bright, Oraegbunam, Chukwuebuka, Awo-Ndubuisi, Esther Chidinma, Osuagwu, Akudo Amarachukwu, Nmezi, Obioha
The Igbo language is facing a risk of becoming endangered, as indicated by a 2025 UNESCO study. This highlights the need to develop language technologies for Igbo to foster communication, learning and preservation. To create robust, impactful, and wi
Externí odkaz:
http://arxiv.org/abs/2405.00997
Autor:
Owodunni, Abraham Toluwase, Yadavalli, Aditya, Emezue, Chris Chinenye, Olatunji, Tobi, Mbataku, Clinton C
Despite advancements in speech recognition, accented speech remains challenging. While previous approaches have focused on modeling techniques or creating accented speech datasets, gathering sufficient data for the multitude of accents, particularly
Externí odkaz:
http://arxiv.org/abs/2402.01152
Autor:
Turki, Houcemeddine, Etori, Naome A., Taieb, Mohamed Ali Hadj, Omotayo, Abdul-Hakeem, Emezue, Chris Chinenye, Aouicha, Mohamed Ben, Awokoya, Ayodele, Lawan, Falalu Ibrahim, Nixdorf, Doreen
This paper investigates the role of text categorization in streamlining stopword extraction in natural language processing (NLP), specifically focusing on nine African languages alongside French. By leveraging the MasakhaNEWS, African Stopwords Proje
Externí odkaz:
http://arxiv.org/abs/2401.13398
Autor:
Houcemeddine Turki, Bonaventure F. P. Dossou, Chris Chinenye Emezue, Abraham Toluwase Owodunni, Mohamed Ali Hadj Taieb, Mohamed Ben Aouicha, Hanen Ben Hassen, Afif Masmoudi
Publikováno v:
Journal of Biomedical Semantics, Vol 15, Iss 1, Pp 1-28 (2024)
Abstract Biomedical relation classification has been significantly improved by the application of advanced machine learning techniques on the raw texts of scholarly publications. Despite this improvement, the reliance on large chunks of raw text make
Externí odkaz:
https://doaj.org/article/17730408430a4d3fa54beb1284027b36
Autor:
Olatunji, Tobi, Afonja, Tejumade, Yadavalli, Aditya, Emezue, Chris Chinenye, Singh, Sahib, Dossou, Bonaventure F. P., Osuchukwu, Joanne, Osei, Salomey, Tonja, Atnafu Lambebo, Etori, Naome, Mbataku, Clinton
Africa has a very low doctor-to-patient ratio. At very busy clinics, doctors could see 30+ patients per day -- a heavy patient burden compared with developed countries -- but productivity tools such as clinical automatic speech recognition (ASR) are
Externí odkaz:
http://arxiv.org/abs/2310.00274
The practical utility of causality in decision-making is widespread and brought about by the intertwining of causal discovery and causal inference. Nevertheless, a notable gap exists in the evaluation of causal discovery methods, where insufficient e
Externí odkaz:
http://arxiv.org/abs/2307.04988
Autor:
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
Externí odkaz:
http://arxiv.org/abs/2304.12155
Autor:
Olatunji, Tobi, Afonja, Tejumade, Dossou, Bonaventure F. P., Tonja, Atnafu Lambebo, Emezue, Chris Chinenye, Rufai, Amina Mardiyyah, Singh, Sahib
Useful conversational agents must accurately capture named entities to minimize error for downstream tasks, for example, asking a voice assistant to play a track from a certain artist, initiating navigation to a specific location, or documenting a la
Externí odkaz:
http://arxiv.org/abs/2306.00253
Autor:
Dione, Cheikh M. Bamba, Adelani, David, Nabende, Peter, Alabi, Jesujoba, Sindane, Thapelo, Buzaaba, Happy, Muhammad, Shamsuddeen Hassan, Emezue, Chris Chinenye, Ogayo, Perez, Aremu, Anuoluwapo, Gitau, Catherine, Mbaye, Derguene, Mukiibi, Jonathan, Sibanda, Blessing, Dossou, Bonaventure F. P., Bukula, Andiswa, Mabuya, Rooweither, Tapo, Allahsera Auguste, Munkoh-Buabeng, Edwin, Koagne, victoire Memdjokam, Kabore, Fatoumata Ouoba, Taylor, Amelia, Kalipe, Godson, Macucwa, Tebogo, Marivate, Vukosi, Gwadabe, Tajuddeen, Elvis, Mboning Tchiaze, Onyenwe, Ikechukwu, Atindogbe, Gratien, Adelani, Tolulope, Akinade, Idris, Samuel, Olanrewaju, Nahimana, Marien, Musabeyezu, Théogène, Niyomutabazi, Emile, Chimhenga, Ester, Gotosa, Kudzai, Mizha, Patrick, Agbolo, Apelete, Traore, Seydou, Uchechukwu, Chinedu, Yusuf, Aliyu, Abdullahi, Muhammad, Klakow, Dietrich
In this paper, we present MasakhaPOS, the largest part-of-speech (POS) dataset for 20 typologically diverse African languages. We discuss the challenges in annotating POS for these languages using the UD (universal dependencies) guidelines. We conduc
Externí odkaz:
http://arxiv.org/abs/2305.13989