Enhancing Automatic English Word Analysis and Prediction using Higher-Order N-Gram Models

The two components that make up the Text Mining framework are: Knowledge distillation is the process of extracting recurring themes or information from the intermediate form and Text cleansing is the one which creates an intermediate form from unstructured text documents form. Although there are numerous precise techniques for recognizing the language in lengthy text samples, it has however remained difficult to identify very small sequences. The extraction process selects sentences with the highest weight in the retrieved document and concatenates them to generate a summary version of the original document without altering the main text. This paper proposes a method to enhance automatic English word analysis and prediction by leveraging higher-order N-gram models. Existing N-gram models face limitations in capturing long-range dependencies in language due to their short context windows. This study, explores the use of higher-order N-grams, such as 4-grams and 5-grams, to extend the context window and improve the predictive power of the model. Present a detailed approach for training these higher-order N-gram models using a large corpus of English text data. Additionally, discuss challenges associated with sparsity and propose strategies for mitigating them, including smoothing techniques. Results from experiments show that the proposed strategy greatly improves the accuracy and effectiveness of automatic word analysis, prediction, and sentiment analysis tasks compared to existing N-gram models. This research contributes to advancing the field of natural language processing by providing a robust framework for incorporating higher-order linguistic dependencies into language models.