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        <title>T20NGD: Annotated corpus for news headlines classification in low resource language,Telugu.</title>
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        <namePart type="given">Mallikarjuna</namePart>
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            <title>Proceedings of the 20th International Conference on Natural Language Processing (ICON)</title>
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            <namePart type="given">Pawar</namePart>
            <namePart type="family">Jyoti</namePart>
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    <abstract>News classification allows analysts and researchers to study trends over time. Based on classification, news platforms can provide readers with related articles. Many digital news platforms and apps use classification to offer personalized content for their users. While there are numerous resources accessible for news classification in various Indian languages, there is still a lack of extensive benchmark dataset specifically for the Telugu language. Our paper presents and describes the Telugu20news group dataset, where news has been collected from various online Telugu news channels. We describe in detail the accumulation and annotation of the proposed news headlines dataset. In addition, we conducted extensive experiments on our proposed news headlines dataset in order to deliver solid baselines for future work.</abstract>
    <identifier type="citekey">chindukuri-sangeetha-2023-t20ngd</identifier>
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        <url>https://aclanthology.org/2023.icon-1.35</url>
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        <date>2023-12</date>
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            <start>423</start>
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%0 Conference Proceedings
%T T20NGD: Annotated corpus for news headlines classification in low resource language,Telugu.
%A Chindukuri, Mallikarjuna
%A Sangeetha, Sivanesan
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F chindukuri-sangeetha-2023-t20ngd
%X News classification allows analysts and researchers to study trends over time. Based on classification, news platforms can provide readers with related articles. Many digital news platforms and apps use classification to offer personalized content for their users. While there are numerous resources accessible for news classification in various Indian languages, there is still a lack of extensive benchmark dataset specifically for the Telugu language. Our paper presents and describes the Telugu20news group dataset, where news has been collected from various online Telugu news channels. We describe in detail the accumulation and annotation of the proposed news headlines dataset. In addition, we conducted extensive experiments on our proposed news headlines dataset in order to deliver solid baselines for future work.
%U https://aclanthology.org/2023.icon-1.35
%P 423-432

Markdown (Informal)

[T20NGD: Annotated corpus for news headlines classification in low resource language,Telugu.](https://aclanthology.org/2023.icon-1.35) (Chindukuri & Sangeetha, ICON 2023)

• T20NGD: Annotated corpus for news headlines classification in low resource language,Telugu. (Chindukuri & Sangeetha, ICON 2023)

ACL

• Mallikarjuna Chindukuri and Sivanesan Sangeetha. 2023. T20NGD: Annotated corpus for news headlines classification in low resource language,Telugu.. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 423–432, Goa University, Goa, India. NLP Association of India (NLPAI).