@inproceedings{nadella-etal-2023-enhancing,
title = "Enhancing {T}elugu News Understanding: Comparative Study of {ML} Algorithms for Category Prediction",
author = "Nadella, Manish Rama Gopal and
Garapati, Venkata Krishna Rayalu and
S.k., Eswar Sudhan and
Jangala, Gouthami and
K.p., Soman and
Kumar, Sachin",
editor = "Chakravarthi, Bharathi R. and
Priyadharshini, Ruba and
M, Anand Kumar and
Thavareesan, Sajeetha and
Sherly, Elizabeth",
booktitle = "Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.dravidianlangtech-1.14",
pages = "108--115",
abstract = "As one of the most extensively used languages in India, Telugu has a sizable audience and a huge library of news articles. Predicting the categories of Telugu news items not only helps with efficient organization but also makes it possible to do trend research, advertise in a certain demographic, and provide individualized recommendations. In order to identify the most effective method for accurate Telugu news category prediction, this study compares and contrasts various machine learning (ML) techniques, including support vector machines (SVM), random forests, and naive Bayes. Accuracy, precision, recall, and F1-score will be utilized as performance indicators to gauge how well these algorithms perform. The outcomes of this comparative analysis will address the particular difficulties and complexities of the Telugu language and add to the body of knowledge on news category prediction. For Telugu-speaking consumers, the study intends to improve news organization and recommendation systems, giving them more relevant and customized news consumption experiences. Our result emphasize that, although other models can be taken into account for further research and comparison, W2Vec-skip gram with polynomial SVM is the best performing combination.",
}
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<abstract>As one of the most extensively used languages in India, Telugu has a sizable audience and a huge library of news articles. Predicting the categories of Telugu news items not only helps with efficient organization but also makes it possible to do trend research, advertise in a certain demographic, and provide individualized recommendations. In order to identify the most effective method for accurate Telugu news category prediction, this study compares and contrasts various machine learning (ML) techniques, including support vector machines (SVM), random forests, and naive Bayes. Accuracy, precision, recall, and F1-score will be utilized as performance indicators to gauge how well these algorithms perform. The outcomes of this comparative analysis will address the particular difficulties and complexities of the Telugu language and add to the body of knowledge on news category prediction. For Telugu-speaking consumers, the study intends to improve news organization and recommendation systems, giving them more relevant and customized news consumption experiences. Our result emphasize that, although other models can be taken into account for further research and comparison, W2Vec-skip gram with polynomial SVM is the best performing combination.</abstract>
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%0 Conference Proceedings
%T Enhancing Telugu News Understanding: Comparative Study of ML Algorithms for Category Prediction
%A Nadella, Manish Rama Gopal
%A Garapati, Venkata Krishna Rayalu
%A S.k., Eswar Sudhan
%A Jangala, Gouthami
%A K.p., Soman
%A Kumar, Sachin
%Y Chakravarthi, Bharathi R.
%Y Priyadharshini, Ruba
%Y M, Anand Kumar
%Y Thavareesan, Sajeetha
%Y Sherly, Elizabeth
%S Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F nadella-etal-2023-enhancing
%X As one of the most extensively used languages in India, Telugu has a sizable audience and a huge library of news articles. Predicting the categories of Telugu news items not only helps with efficient organization but also makes it possible to do trend research, advertise in a certain demographic, and provide individualized recommendations. In order to identify the most effective method for accurate Telugu news category prediction, this study compares and contrasts various machine learning (ML) techniques, including support vector machines (SVM), random forests, and naive Bayes. Accuracy, precision, recall, and F1-score will be utilized as performance indicators to gauge how well these algorithms perform. The outcomes of this comparative analysis will address the particular difficulties and complexities of the Telugu language and add to the body of knowledge on news category prediction. For Telugu-speaking consumers, the study intends to improve news organization and recommendation systems, giving them more relevant and customized news consumption experiences. Our result emphasize that, although other models can be taken into account for further research and comparison, W2Vec-skip gram with polynomial SVM is the best performing combination.
%U https://aclanthology.org/2023.dravidianlangtech-1.14
%P 108-115
Markdown (Informal)
[Enhancing Telugu News Understanding: Comparative Study of ML Algorithms for Category Prediction](https://aclanthology.org/2023.dravidianlangtech-1.14) (Nadella et al., DravidianLangTech-WS 2023)
ACL
- Manish Rama Gopal Nadella, Venkata Krishna Rayalu Garapati, Eswar Sudhan S.k., Gouthami Jangala, Soman K.p., and Sachin Kumar. 2023. Enhancing Telugu News Understanding: Comparative Study of ML Algorithms for Category Prediction. In Proceedings of the Third Workshop on Speech and Language Technologies for Dravidian Languages, pages 108–115, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.