Bengali Text Categorization Based on Deep Hybrid CNN–LSTM Network with Word Embedding

Alam, Samrat; Haque, Md Afnan Ul; Rahman, Ashiqur

doi:10.1109/iciset54810.2022.9775913

Cited by 5 publications

(1 citation statement)

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“…In the research work [25], the authors have taken data from open resources which are containing the Bangla newspaper article corpus with 12 categories. For categorizing these article datasets, they have used word embedding methods to extract features from raw data and then used deep learning methods: Convolutional Neural Network (CNN), Artificial Neural Network (ANN), Long Short-term memory (LSTM) and Hybrid (CNN+Bi-LSTM) methods to classify them based on their contents.…”

Section: Mandal and Senmentioning

confidence: 99%

Bengali Text Classification: A New multi-class Dataset and Performance Evaluation of Machine Learning and Deep Learning Models

Roy

Sarkar

Mandal

2023

Preprint

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This study focuses on Bengali text classification using machine learning and deep learning techniques. Text classification is a fundamental task in natural language processing (NLP) that involves categorizing text documents into predefined categories or classes. While text classification has received considerable attention in the English language, there is a limited amount of research specifically addressing Bengali text classification. This gap in the literature highlights the need for exploring and developing effective techniques tailored to the Bengali language. Furthermore, although some datasets for Bengali text classification tasks have been produced, most of the datasets have a limited number of labels. In our work, we introduce a new dataset for the Bengali text classification task, which has 38 class labels. The dataset includes data from the leading Bengali newspapers. We have evaluated many state-of-the-art machine learning and deep learning classification methods on our dataset to construct a benchmark that will facilitate future research.

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Section: Mandal and Senmentioning

confidence: 99%