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            <title>Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era</title>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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        <name type="personal">
            <namePart type="given">Maria</namePart>
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    <abstract>In this paper, we showcase a comprehensive end-to-end pipeline for creating a superior Bartangi language corpus and using it for training word embeddings. The critically low-resource Pamiri lan- guage of Bartangi, which is spoken in Tajikistan, has difficulties such as morphological complexity, orthographic variety, and a lack of data. In order to overcome these obstacles, we gathered a raw corpus of roughly 6,550 phrases, used the Uniparser-Morph-Bartangi morphological analyzer for linguistically accurate lemmatization, and implemented a thorough cleaning procedure to eliminate noise and ensure proper tokenization. The lemmatized corpus that results greatly lowers word spar- sity and raises the standard of linguistic analysis.The processed corpus was then used to train two different Word2Vec models, Skip-gram and CBOW, with a vector size of 100, a context window of 5, and a minimum frequency threshold of 1. The resultant word embeddings were displayed using dimensionality reduction techniques like PCA and t-SNE, and assessed using intrinsic methods like nearest-neighbor similarity tests. Our tests show that even from tiny datasets, meaningful semantic representations can be obtained by combining informed morphological analysis with clean prepro- cessing. One of the earliest computational datasets for Bartangi, this resource serves as a vital basis for upcoming NLP tasks, such as language modeling, semantic analysis, and low-resource machine translation. To promote more research in Pamiri and other under-represented languages, we make the corpus, lemmatizer pipeline, and trained embeddings publicly available.</abstract>
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%0 Conference Proceedings
%T Building a Clean Bartangi Language Corpus and Training Word Embeddings for Low-Resource Language Modeling
%A Tariq, Warda
%A Popov, Victor
%A Gromov, Vasilii
%Y Angelova, Galia
%Y Kunilovskaya, Maria
%Y Escribe, Marie
%Y Mitkov, Ruslan
%S Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era
%D 2025
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F tariq-etal-2025-building
%X In this paper, we showcase a comprehensive end-to-end pipeline for creating a superior Bartangi language corpus and using it for training word embeddings. The critically low-resource Pamiri lan- guage of Bartangi, which is spoken in Tajikistan, has difficulties such as morphological complexity, orthographic variety, and a lack of data. In order to overcome these obstacles, we gathered a raw corpus of roughly 6,550 phrases, used the Uniparser-Morph-Bartangi morphological analyzer for linguistically accurate lemmatization, and implemented a thorough cleaning procedure to eliminate noise and ensure proper tokenization. The lemmatized corpus that results greatly lowers word spar- sity and raises the standard of linguistic analysis.The processed corpus was then used to train two different Word2Vec models, Skip-gram and CBOW, with a vector size of 100, a context window of 5, and a minimum frequency threshold of 1. The resultant word embeddings were displayed using dimensionality reduction techniques like PCA and t-SNE, and assessed using intrinsic methods like nearest-neighbor similarity tests. Our tests show that even from tiny datasets, meaningful semantic representations can be obtained by combining informed morphological analysis with clean prepro- cessing. One of the earliest computational datasets for Bartangi, this resource serves as a vital basis for upcoming NLP tasks, such as language modeling, semantic analysis, and low-resource machine translation. To promote more research in Pamiri and other under-represented languages, we make the corpus, lemmatizer pipeline, and trained embeddings publicly available.
%U https://aclanthology.org/2025.ranlp-1.145/
%P 1256-1262

Markdown (Informal)

[Building a Clean Bartangi Language Corpus and Training Word Embeddings for Low-Resource Language Modeling](https://aclanthology.org/2025.ranlp-1.145/) (Tariq et al., RANLP 2025)

• Building a Clean Bartangi Language Corpus and Training Word Embeddings for Low-Resource Language Modeling (Tariq et al., RANLP 2025)

ACL

• Warda Tariq, Victor Popov, and Vasilii Gromov. 2025. Building a Clean Bartangi Language Corpus and Training Word Embeddings for Low-Resource Language Modeling. In Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era, pages 1256–1262, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.