Towards a morphological transducer and orthography converter for Western Tlacolula Valley Zapotec

Washington, Jonathan North; Lopez, Felipe H.; Lillehaugen, Brook Danielle

doi:10.18653/v1/2021.americasnlp-1.21

Cited by 1 publication

(1 citation statement)

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“…FSTs have been implemented in many lowresource settings, as well as for the application of orthographic conversion, transliteration, and text normalization. Washington et al (2021) developed a transducer to assist in orthographic conversion and morphological analysis of Zapotec and found that even an incomplete transducer could yield positive results. Similar efforts use an FST to develop a morphological generator and analyzer while simultaneously addressing the issue of missing diacritics (Alkhairy et al, 2020), demonstrating the easy expansion of an FST to create more resources for a language.…”

Section: Finite State Transducersmentioning

confidence: 99%

Comparing methods of orthographic conversion for Bàsàá, a language of Cameroon

O’neil,

Swanson,

Pugh

et al. 2023

Proceedings of the Fourth Workshop on Resources for African Indigenous Languages (RAIL 2023)

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Orthographic standardization is a milestone in a language's documentation and the development of its resources. However, texts written in former orthographies remain relevant to the language's history and development and therefore must be converted to the standardized orthography. Ensuring a language has access to the orthographically standardized version of all of its recorded texts is important in the development of resources as it provides additional textual resources for training, supports contribution of authors using former writing systems, and provides information about the development of the language. This paper evaluates the performance of natural language processing methods, specifically Finite State Transducers and Long Short-term Memory networks, for the orthographical conversion of Bàsàá texts from the Protestant missionary orthography to the now-standard AGLC orthography, with the conclusion that LSTMs are somewhat more effective in the absence of explicit lexical information.

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Section: Finite State Transducersmentioning

confidence: 99%