Finite-state morphological transducers for three Kypchak languages
By Jonathan North Washington, Ilnar Salimzyanov, Francis M. Tyers. 2014
39 citations recorded by the source corpus.
This paper describes the development of free/open-source finite-state morphological transducers for three Turkic languages―Kazakh, Tatar, and Kumyk―representing one language from each of the three sub-branches of the Kypchak branch of Turkic. The finite-state toolkit used for the work is the Helsinki Finite-State Toolkit (HFST). This paper describes how the development of a transducer for each subsequent closely-related language took less development time. An evaluation is presented which shows that the transducers all have a reasonable coverage―around 90\%―on freely available corpora of the languages, and high precision over a manually verified test set.
Subfields: Artificial Intelligence, Experimental and Cognitive Psychology, Natural Language Processing
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Citations in the corpus
- Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data (2019, positive): “For rule-based segmentation, Apertium-kaz (Washington et al., 2014) was used.”