Summary of Enhancing Bangla Language Next Word Prediction and Sentence Completion Through Extended Rnn with Bi-lstm Model on N-gram Language, by Md Robiul Islam et al.
Enhancing Bangla Language Next Word Prediction and Sentence Completion through Extended RNN with Bi-LSTM Model On N-gram Language
by Md Robiul Islam, Al Amin, Aniqua Nusrat Zereen
First submitted to arxiv on: 3 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Bi-LSTM model effectively handles Bangla next-word prediction and sentence generation, demonstrating its versatility and potential impact in the field of Natural Language Processing (NLP). By introducing a new approach to predict following words and complete sentences, this paper expands the scope of Bangla language processing. The model is trained on a corpus dataset constructed from various news portals, including bdnews24, BBC News Bangla, and Prothom Alo. The results show significant improvement over existing methods, achieving 99% accuracy for both 4-gram and 5-gram word predictions, as well as high accuracy rates for uni-gram, bi-gram, and tri-gram word prediction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new Bi-LSTM model that can predict Bangla words and complete sentences. This makes it easier to write texts and communicate in the Bangla language. The researchers trained their model on a big dataset of news articles from Bangladesh and other countries. They tested the model and found that it was much better than existing methods at predicting words. |
Keywords
» Artificial intelligence » Lstm » Natural language processing » Nlp
