T 20 NGD : Annotated corpus for news headlines classification in low resource language, T elugu. (2023)

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.

## introduction in the digital era of today, the quantity of news content is overwhelming. globally, thousands of articles are published every minute on a wide variety of topics, ranging from international politics to local athletics. as the quantity of news grows exponentially, the need for effective and efficient classification of news becomes paramount. this ensures that readers are presented with content pertinent to their interests and that platforms can manage and distribute their vast content libraries effectively. news categorization is an important aspect of news research @xcite . we utilize the information provided by the news to categorize them into various groups @xcite . some work has been done on news classification; for instance, there are news datasets like 20newsgroup @xcite and ag news @xcite . numerous methodologies have been devised in recent studies to evaluate the classification of news texts and the difficulties associated with it. however, the majority of these works are restricted to a small number of languages, with english being the predominant language. in spite of the fact that telugu is the fourth most spoken language in india and the sixteenth most spoken language in the globe @xcite , the classification of news text in telugu remains unexplored. there have been some resources created in telugu supported for various nlp tasks such as sentiment analysis @xcite @xcite , sentiment emotion @xcite , hate speech detection @xcite , and for code-mixing @xcite . however, there is no gold standard benchmark dataset for supporting telugu news headlines text classification task. therefore through this paper, we present a benchmark dataset (t20ngd) for telugu news headlines text classification that has been meticulously annotated to serve as a gold standard for news headlines classification. the structure of the paper is arranged as. in part 2, we review prior telugu text classification approaches, existing telugu corpus designed for nlp tasks, and recent improvements. in section 3, we outline our corpus and annotation methodology. sec. 4 briefly describes the models evaluated over the proposed dataset. section 5 outlines experiments and results discussion made in section 6. finally, our conclusions are presented in section 7. ## related study numerous approaches have been employed to classify news headlines in indian languages, with a limited number of studies focusing on the telugu language. this section provides an overview of the ## dataset construction the process of constructing the dataset involved distinct phases: the collection of news headlines, preprocessing and the subsequent annotation of these pre-processed headlines with their corresponding labels. ## experimental results after completion of the annotation process, we finally had a dataset with 29744 news headlines. in order to advance with the experimental procedure, the dataset has been divided into train, validation, and test sets. ## results discussion figure 3 shows the confusion matrix of murilbase-cased model over t20news headlines dataset. based on the this, we present a concise error analysis of the muril-base-cased model as follows. ## conclusion this paper presents the development of an extensive dataset for the purpose of text classification of telugu news headlines.data was gathered from a diverse range of online news publishing platforms, afterwards subjected to pre-processing techniques, and ultimately utilized to construct an annotated corpus. various models were utilized in our study to classify telugu news headlines text, employing multiple word embeddings methodologies. in this study, we have successfully constructed baselines on proposed dataset for the purpose of text classification. this has allowed us to create and evaluate a reliable dataset specifically designed for a lowresource language, such as telugu.