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• W3114257334 abstract "Genre detection from news articles or news titles is one kind of text classification procedures where news articles or titles are categorized among different families. Nowadays, text classification has become a key research field in text mining and natural language understanding because of it’s several applications, such as search engine, document filtering, keywords extraction, text summarizing, etc. Several studies have been conducted for detecting news genres in different languages, but little research has been done in Bangla language due to the lack of Bangla resources. Moreover, in the few works of Bangla language, a little number of news genres have been used for classifications and dataset has been created from a small number of news sources. In this study, we have shown comparative analysis of different traditional machine learning, advanced neural networks, attention-based supervised learning models by extracting several informative features including TF-IDF, category-based word frequency, word embedding, etc. We have implemented these categorization methods for both news article and news title classifications against ten mutually exclusive news genres. From our analysis, We have found that ‘Bidirectional LSTM’ with ‘Word2vec’ feature extraction technique using ‘Skip-gram’ method is the most robust method for both classifications, compared to other models." @default.
• W3114257334 created "2021-01-05" @default.
• W3114257334 creator A5001990947 @default.
• W3114257334 date "2020-12-22" @default.
• W3114257334 modified "2023-09-27" @default.
• W3114257334 title "A Benchmark Study on Machine Learning Methods using Several Feature Extraction Techniques for News Genre Detection from Bangla News Articles & Titles" @default.
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• W3114257334 doi "https://doi.org/10.1145/3428363.3428373" @default.
• W3114257334 hasPublicationYear "2020" @default.
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