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        <title>Lexicon Induction for Spoken Rusyn – Challenges and Results</title>
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            <namePart type="given">Tomaž</namePart>
            <namePart type="family">Erjavec</namePart>
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            <namePart type="given">Lidia</namePart>
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    <abstract>This paper reports on challenges and results in developing NLP resources for spoken Rusyn. Being a Slavic minority language, Rusyn does not have any resources to make use of. We propose to build a morphosyntactic dictionary for Rusyn, combining existing resources from the etymologically close Slavic languages Russian, Ukrainian, Slovak, and Polish. We adapt these resources to Rusyn by using vowel-sensitive Levenshtein distance, hand-written language-specific transformation rules, and combinations of the two. Compared to an exact match baseline, we increase the coverage of the resulting morphological dictionary by up to 77.4% relative (42.9% absolute), which results in a tagging recall increased by 11.6% relative (9.1% absolute). Our research confirms and expands the results of previous studies showing the efficiency of using NLP resources from neighboring languages for low-resourced languages.</abstract>
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%0 Conference Proceedings
%T Lexicon Induction for Spoken Rusyn – Challenges and Results
%A Rabus, Achim
%A Scherrer, Yves
%Y Erjavec, Tomaž
%Y Piskorski, Jakub
%Y Pivovarova, Lidia
%Y Šnajder, Jan
%Y Steinberger, Josef
%Y Yangarber, Roman
%S Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F rabus-scherrer-2017-lexicon
%X This paper reports on challenges and results in developing NLP resources for spoken Rusyn. Being a Slavic minority language, Rusyn does not have any resources to make use of. We propose to build a morphosyntactic dictionary for Rusyn, combining existing resources from the etymologically close Slavic languages Russian, Ukrainian, Slovak, and Polish. We adapt these resources to Rusyn by using vowel-sensitive Levenshtein distance, hand-written language-specific transformation rules, and combinations of the two. Compared to an exact match baseline, we increase the coverage of the resulting morphological dictionary by up to 77.4% relative (42.9% absolute), which results in a tagging recall increased by 11.6% relative (9.1% absolute). Our research confirms and expands the results of previous studies showing the efficiency of using NLP resources from neighboring languages for low-resourced languages.
%R 10.18653/v1/W17-1405
%U https://aclanthology.org/W17-1405/
%U https://doi.org/10.18653/v1/W17-1405
%P 27-32

Markdown (Informal)

[Lexicon Induction for Spoken Rusyn – Challenges and Results](https://aclanthology.org/W17-1405/) (Rabus & Scherrer, BSNLP 2017)

• Lexicon Induction for Spoken Rusyn – Challenges and Results (Rabus & Scherrer, BSNLP 2017)

ACL

• Achim Rabus and Yves Scherrer. 2017. Lexicon Induction for Spoken Rusyn – Challenges and Results. In Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing, pages 27–32, Valencia, Spain. Association for Computational Linguistics.