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Transformer-based Model for Word Level Language Identification in Code-mixed Kannada-English Texts

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arxiv 2211.14459 v1 pith:EWNLD2NF submitted 2022-11-26 cs.CL cs.AI

Atnafu Lambebo Tonja , Mesay Gemeda Yigezu , Olga Kolesnikova , Moein Shahiki Tash , Grigori Sidorov , Alexander Gelbuk This is my paper · ORCID
classification cs.CLcs.AI
keywords code-mixedlanguageidentificationmodelf1-scoremediasocialtexts
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Using code-mixed data in natural language processing (NLP) research currently gets a lot of attention. Language identification of social media code-mixed text has been an interesting problem of study in recent years due to the advancement and influences of social media in communication. This paper presents the Instituto Polit\'ecnico Nacional, Centro de Investigaci\'on en Computaci\'on (CIC) team's system description paper for the CoLI-Kanglish shared task at ICON2022. In this paper, we propose the use of a Transformer based model for word-level language identification in code-mixed Kannada English texts. The proposed model on the CoLI-Kenglish dataset achieves a weighted F1-score of 0.84 and a macro F1-score of 0.61.

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Cited by 2 Pith papers

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  1. Bilingual Word Level Language Identification for Omotic Languages

    cs.CL 2025-09 conditional novelty 4.0 of 10

    On a new 144,000-word annotated dataset for Wolayta and Gofa, BERT-base-uncased embeddings with an LSTM classifier reach 0.72 F1, the best of seven compared approaches.

  2. Multiclass Sentiment Analysis for Identifying Political Viewpoints

    cs.CL 2026-08 conditional novelty 3.0 of 10

    On the DravidianLangTech 2025 Tamil political sentiment test set, XGBoost scored 0.2835 macro F1 and BERT scored 0.2806, showing the task is far from solved.

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