An LSTM –based approach for predicting the next word in Arabic language
DOI:
https://doi.org/10.29304/jqcsm.2026.18.12502Keywords:
Next word prediction, Arabic language, Deep learning, Recurrent neural network, LSTM, Language modeling, Next word suggestion technique, Natural, language processing, CorpusAbstract
Next word prediction, or language modeling, is a crucial task in natural language processing (NLP) that streamlines the typing process by recommending the subsequent word, hence saving time during conversations. The method of next word prediction is intricate, as the machine must anticipate the user's thoughts. A recurrent neural network (RNN), specifically Long Short-Term Memory (LSTM), comprehends previous text and forecasts subsequent words, aiding the user in sentence construction. This study seeks to employ RNN, specifically LSTM, to forecast the subsequent word in one of the most intricate languages with limited resources (Arabic). The results indicated an accuracy of 97.49% and loss of 0.5155%, suggesting that it is sufficiently effective for predicting subsequent Arabic words.
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