Abstract
In this paper, Bengali text information has been utilized for predicting the next word contingent based on the previous one. To do that, one should consider two key aspects such as the natural language processing (NLP) stage and the word predicting stage. When both work together, the system gets a new predicted word that is relevant to the previous word. For achieving such correct predicted words, long short-term memory (LSTM) has been used which is best known for its memory management. LSTM embeds the input words and fits them into the model, then after successful training of the model, it can predict the next word from a given sentence. The user can also initialize the number of predicted words. This paper gives an overview of word prediction for the Bengali language based on LSTM and describes the database integration and proposed approach obtained 97.60% accuracy.
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References
Sarker S, Islam ME, Saurav JR, Nahid MM (2020) Word completion and sequence prediction in Bangla language using trie and a hybrid approach of sequential LSTM and N-gram. In: 2nd international conference on advanced information and communication technology (ICAICT), pp 162–167
Rakib OF, Akter S, Khan MA, Das AK, Habibullah KM (2019) Bangla word prediction and sentence completion using GRU: an extended version of RNN on N-gram language model. In: International conference on sustainable technologies for industry 4.0 (STI), pp 1–6
Mikolov T, Yih WT, Zweig G (2013) Linguistic regularities in continuous space word representations. In: Conference of the North American chapter of the association for computational linguistics: human language technologies, pp 746–751
Barman PP, Boruah A (2018) A RNN based Approach for next word prediction in Assamese phonetic transcription. Procedia Comput Sci 143:117–123
Abujar S, Masum AK, Chowdhury SM, Hasan M, Hossain SA (2019) Bengali text generation using bi-directional RNN. In: 10th international conference on computing, communication and networking technologies (ICCCNT), pp 1–5
Mnih A, Kavukcuoglu K (2013) Learning word embeddings efficiently with noise-contrastive estimation. In: Proceedings of the 26th international conference on neural information processing systems, vol 2. pp 2265–2273
Sundermeyer M, Schlüter R, Ney H (2012) LSTM neural networks for language modeling. In: Thirteenth annual conference of the international speech communication association
El-Qawasmeh E (2004) Word prediction via a clustered optimal binary search tree. Int Arab J Inf Technol 1
Al-Mubaid H (2007) A learning-classification based approach for word prediction. Int Arab J Inf Technol 4:264–271
Abbas Q (2015) A stochastic prediction interface for Urdu. Int J Intell Syst Appl (IJISA) 7:94–100
Prasad PD, Sunitha KV, Rani BP (2019) Word N-gram based approach for word sense disambiguation in Telugu natural language processing. Int J Recent Technol Eng (IJRTE) 7
Karthigaikumar P (2021) Industrial quality prediction system through data mining algorithm. J Electron Inform 3:126–137
Shakya S, Smys S (2021) Big data analytics for improved risk management and customer segregation in banking applications. J ISMAC 3:235–249
Jahnavi A, Dushyanth Reddy B, Kommineni M, Haldorai A, Vasantha B (2021) Election tweets prediction using enhanced cart and random forest. In: Inventive computation and information technologies, pp 851–858. Springer, Singapore
Haque M, Habib M, Rahman M (2015) Automated word prediction in Bangla language using stochastic language models. Int J Found Comput Sci Technol 5(6):67–75
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Hasan, M., Sakib, N., Hridoy, R.H., Ananto, N.H., Akhter, S., Habib, M.T. (2023). An LSTM-Based Word Prediction in Bengali. In: Ranganathan, G., Fernando, X., Rocha, Á. (eds) Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 383. Springer, Singapore. https://doi.org/10.1007/978-981-19-4960-9_70
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DOI: https://doi.org/10.1007/978-981-19-4960-9_70
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