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Innovations and Interdisciplinary Solutions for Underserved Areas. 4th EAI International Conference, InterSol 2020, Nairobi, Kenya, March 8-9, 2020, Proceedings

Research Article

Building Word Representations for Wolof Using Neural Networks

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  • @INPROCEEDINGS{10.1007/978-3-030-51051-0_20,
        author={Alla Lo and Cheikh M. Bamba Dione and Elhadji Mamadou Nguer and Sileye O. Ba and Moussa Lo},
        title={Building Word Representations for Wolof Using Neural Networks},
        proceedings={Innovations and Interdisciplinary Solutions for Underserved Areas. 4th EAI International Conference, InterSol 2020, Nairobi, Kenya, March 8-9, 2020, Proceedings},
        proceedings_a={INTERSOL},
        year={2020},
        month={8},
        keywords={Neural network Word embedding Low resource language Wolof},
        doi={10.1007/978-3-030-51051-0_20}
    }
    
  • Alla Lo
    Cheikh M. Bamba Dione
    Elhadji Mamadou Nguer
    Sileye O. Ba
    Moussa Lo
    Year: 2020
    Building Word Representations for Wolof Using Neural Networks
    INTERSOL
    Springer
    DOI: 10.1007/978-3-030-51051-0_20
Alla Lo, Cheikh M. Bamba Dione, Elhadji Mamadou Nguer,*, Sileye O. Ba, Moussa Lo
    *Contact email: elhadjimamadou.nguer@uvs.edu.sn

    Abstract

    Because a large portion of population in rural areas in sub Saharan Africa understand only local languages, they do not have access all to content available in the World Wide Web. Most content are available in English, Spanish, French, etc. Content in low-resource languages such as Wolof, which is mostly spoken in Senegal, are scarce. Automatic systems for natural language understanding such as machine translation systems that can transform information from common to low-resource languages would allow people in rural areas to access relevant scientific or health content.

    Nowadays, word representation is the preliminary step of natural language understanding models. This paper presents investigations we conducted to build Wolof words representation using a corpus gathered from Internet. We applied neural word embedding models to the Wolof language corpus. These models are known to be able to capture into the embedding space semantic an syntactic relations between words. Experiments we conducted suggest that, despite a limited corpus size, our models successfully captures relations between words.

    Keywords
    Neural network Word embedding Low resource language Wolof
    Published
    2020-08-06
    Appears in
    SpringerLink
    http://dx.doi.org/10.1007/978-3-030-51051-0_20
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