# An Enhanced Neural Word Embedding Model for Transfer Learning
## 6 Conclusions  
In this paper, two Bangla FastText word embedding pre-trained models were presented, with a toolbox trained on a huge Bangla corpus (around 20 million points of textual data), including organized and non-organized datasets. In order to evaluate the proposed word embedding performance, three text classification datasets were used and extracted the embedding, and then trained the method with the help of common machine learning classifiers. Next, the performance of the proposed method was compared with the Facebook FastText and Word2 Vec, where the proposed models showed enhanced performance for all datasets. The results show that without preprocessing such as lemmatization, stemming, and other techniques, significant performance improvements can be achieved with BanglaFastText word embedding. BanglaFastText also exceeds Facebook's FastText by a wide margin in text classification and word similarity.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Performance Score**} \\ \cline{3-4}  & & **Existing** & **Proposed** \\ \hline \multirow{2}{*}{**BanFakeNews**} & SVM (\(f1\)-score) & 0.46 & 0.57 \\  & RF (\(f1\)-score) & 0.55 & 0.59 \\  & LR (\(f1\)-score) & 0.53 & 0.60 \\  & LSTM (\(f1\)-score) & 0.53 & 0.66 \\  & CNN (\(f1\)-score) & 0.59 & 0.64 \\ \hline \multirow{2}{*}{**Bangla sentiment analysis**} & SVM(acc) & 0.93 & 0.94 \\  & RF(acc) & 0.89 & 0.91 \\ \cline{1-1}  & LR(acc) & 0.91 & 0.88 \\ \hline \multirow{2}{*}{**Bengali News Comment**} & SVM(acc) & 0.61 & 0.76 \\  & LSTM(acc) & 0.74 & 0.75 \\ \cline{1-1}  & CNN(acc) & 0.61 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of BanglaFastText’s performance with previous experimental datasets.  
This work was carried out by utilizing the following neural word embedding FastText techniques, so it does not consider word position, context, and sensitivity. It always provides similar word vectors for a word in any position. For example, the word 'Python', describing either the programming language or the animal, would be the same vector. This is a limitation of this work.  
Future research direction can be the development of the advanced models using the BangalLM dataset, such as BERT, ELMO, and GTP to overcome the limitations of this work. In addition, a Python-based Bangal toolkit can be developed, which will make it easier to develop linguistic applications and research. Design APIs to access data in multiple applications can also be a good future consideration.  
Conceptualization, M.K., M.S.I.S., M.F.S., N.J.P. and M.S.A.; investigation, M.K., M.S.I.S., M.S.A., P.K.D. and T.K.; methodology, M.K., M.S.I.S., M.F.S., N.J.P., M.S.A., P.K.D. and T.K.; software, M.K., M.S.I.S. and M.F.S.; validation, M.K., M.S.I.S., M.F.S., N.J.P., M.S.A., P.K.D. and T.K.; writing--original draft preparation, M.K., M.S.I.S., M.F.S. and N.J.P.; writing--review and editing, M.K., M.S.A., P.K.D. and T.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The authors declare no conflict of interest.