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A Functional NLP System for Twi (Akan) Using Limited Data

  • David Sasu
    ,
  • Dennis Asamoah Owusu
Research Output:
Journal Article or Conference Article in Journal
Conference article
 Peer-review

Publication Information

Output type

Research Output:
Journal Article or Conference Article in Journal
Conference article
 Peer-review

Original language

English

Journal (Volume, Issue Number)

ACM SIGCAS Conference on Computing and Sustainable Societies

Publication milestones

  • Accepted/In press - 2019
  • Published - 2019

Publication status

Published - 2019

ISSN

4503-9999

Abstract

Natural Language Processing (NLP) continues to advance. How- ever, native Ghanaian languages like many other languages in the world remain untouched. Limited availability of annotated data in these languages is, perhaps, the primary limiting factor. This work explores the development of a useful and practical NLP system for Twi, a native Ghanaian language, under the constraint of limited data. The result is a system developed using only 4.65 minutes of annotated speech data that allows searching for selected Twi songs by saying the song title in Twi.