Sustainable language technology for African languages

Hurskainen, Arvi

doi:10.4324/9781315392981-19

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Most African languages in general would require the use of rule-based methods, since most of them lack large datasets for probabilistic and Machine Learning approaches. The use of rulebased methods can influence the status of African languages, especially in NLP advancements, where linguistic features can be trained for further analysis, which can further open up possibilities for applications such as parsers, machine translators, information retrieval systems, self-tutored language learning systems and so forth [8].…”

Section: Related Workmentioning

confidence: 99%

Ga-RBP: A Rule-based Parser for the Syntactic Analysis of the Ga Language of Ghana

Otto,

Nakpih

2024

Asian J. Res. Com. Sci.

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This research presents a Parts of Speech (POS) corpus and a static rule-based technique, which we refer to as Ga Rule-based Parser (Ga-RBP), for the syntactic analysis and the parsing of sentences for the Ga language of Ghana. The technique is developed to parse sentences by utilising the POS tagged corpus; the corpus was developed by manually tagging the Ga words with their corresponding POS tags following a standard Ga-English dictionary and custom Tagset for the language. The syntax rules were computationally defined using production rules, which establish how a word should follow the other in the right sequence to form a correct grammatical statement based on their POS. The model generally analyses the sentence structure of the language to assert its syntactic state for correctness or otherwise.

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Section: Related Workmentioning

confidence: 99%

Ga-RBP: A Rule-based Parser for the Syntactic Analysis of the Ga Language of Ghana

Otto,

Nakpih

2024

Asian J. Res. Com. Sci.

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“…Not much lnguage technology has been developed for these languages. The main exceptions are the works by Arvi Hurskainen on Swahili (for an overviow and references, see Hurskainen (2018)) and the works by Laurette Pretorius, Sonja Bosch and others for Zulu, Xhosa and other South African languages (e.g. Pretorius & Bosch (2009a), Preto-rius & Bosch (2009b).…”

Section: Morphological Analysers and Dictionariesmentioning

confidence: 99%

GiellaLT: an infrastructure for rule-based language technology tool development

Pirinen

Trosterud

Moshagen

et al. 2023

JDHASA

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Currently, machine learning is presented as the ultimate solution for language technology regardless of use case and application, however, it requires as a starting point a massive amount of curated linguistic data in electronic form that is expected to be high quality and representative of the kind of language usage that the tools will follow. For minority and indigenous languages, this can be an insurmountable task, as digital materials of the necessary sizes do not exist and can not easily be produced. In this article we present an approach we have successfully used for supporting indigenous languages to survive and grow in digital contexts for years, and describe the potential of our approach for African contexts. Our technological solution is a free and open-source infrastructure that enables language experts and users to cooperate on creating linguistic resources like dictionaries and grammatical descriptions. In addition we provide language-independent frameworks to build these into applications that are needed by the language community.

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Sustainable language technology for African languages

Cited by 2 publications

References 16 publications

Ga-RBP: A Rule-based Parser for the Syntactic Analysis of the Ga Language of Ghana

Ga-RBP: A Rule-based Parser for the Syntactic Analysis of the Ga Language of Ghana

GiellaLT: an infrastructure for rule-based language technology tool development

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