Long Short-Term Memory-Based Next Keyword Prediction
K. Rajkumar, S. Karthikeyan, Udhayakumar Hariharan · 2024
This paper focuses on a method that uses an LSTM model to predict the next word of a sentence. Based on Katz's Backoff model, it has been designed to improve and further succeed in our previous work. The main intention here is to show how effective the LSTM model is when compared to Katz's backoff model in terms of the number of n-grams required to predict the next word, and it also helps you predict more accurately. Here LSTM is used for processing, prediction and classification. Since the LSTM model is efficient and has been modified to the task at hand, the model offers very highly accurate predictions even though have used only a tiny portion of the entire corpus. When comparing the existing model, the proposed model predicts the word with accuracy of 90%.