@Article{app12062848,
AUTHOR = {Kowsher, Md. and Sobuj, Md. Shohanur Islam and Shahriar, Md. Fahim and Prottasha, Nusrat Jahan and Arefin, Mohammad Shamsul and Dhar, Pranab Kumar and Koshiba, Takeshi},
TITLE = {An Enhanced Neural Word Embedding Model for Transfer Learning},
JOURNAL = {Applied Sciences},
VOLUME = {12},
YEAR = {2022},
NUMBER = {6},
ARTICLE-NUMBER = {2848},
URL = {https://www.mdpi.com/2076-3417/12/6/2848},
ISSN = {2076-3417},
ABSTRACT = {Due to the expansion of data generation, more and more natural language processing (NLP) tasks are needing to be solved. For this, word representation plays a vital role. Computation-based word embedding in various high languages is very useful. However, until now, low-resource languages such as Bangla have had very limited resources available in terms of models, toolkits, and datasets. Considering this fact, in this paper, an enhanced BanglaFastText word embedding model is developed using Python and two large pre-trained Bangla models of FastText (Skip-gram and cbow). These pre-trained models were trained on a collected large Bangla corpus (around 20 million points of text data, in which every paragraph of text is considered as a data point). BanglaFastText outperformed Facebook’s FastText by a significant margin. To evaluate and analyze the performance of these pre-trained models, the proposed work accomplished text classification based on three popular textual Bangla datasets, and developed models using various machine learning classical approaches, as well as a deep neural network. The evaluations showed a superior performance over existing word embedding techniques and the Facebook Bangla FastText pre-trained model for Bangla NLP. In addition, the performance in the original work concerning these textual datasets provides excellent results. A Python toolkit is proposed, which is convenient for accessing the models and using the models for word embedding, obtaining semantic relationships word-by-word or sentence-by-sentence; sentence embedding for classical machine learning approaches; and also the unsupervised finetuning of any Bangla linguistic dataset.},
DOI = {10.3390/app12062848}
}