Hybrid Model For Word Prediction Using Naive Bayes and Latent Information

Historically, the Natural Language Processing area has been given too much attention by many researchers. One of the main motivation beyond this interest is related to the word prediction problem, which states that given a set words in a sentence, one can recommend the next word. In literature, this problem is solved by methods based on syntactic or semantic analysis. Solely, each of these analysis cannot achieve practical results for end-user applications. For instance, the Latent Semantic Analysis can handle semantic features of text, but cannot suggest words considering syntactical rules. On the other hand, there are models that treat both methods together and achieve state-of-the-art results, e.g. Deep Learning. These models can demand high computational effort, which can make the model infeasible for certain types of applications. With the advance of the technology and mathematical models, it is possible to develop faster systems with more accuracy. This work proposes a hybrid word suggestion model, based on Naive Bayes and Latent Semantic Analysis, considering neighbouring words around unfilled gaps. Results show that this model could achieve 44.2% of accuracy in the MSR Sentence Completion Challenge

A Learning-Classification Based Approach for Word Prediction

Abstract: Word prediction is an important NLP problem in which we want to predict the correct word in a given context. Word completion utilities, predictive text entry systems, writing aids, and language translation are some of common word prediction applications. This paper presents a new word prediction approach based on context features and machine learning. The proposed method casts the problem as a learning-classification task by training word predictors with highly discriminating features selected by various feature selection techniques. The contribution of this work lies in the new way of presenting this problem, and the unique combination of a top performer in machine learning, svm, with various feature selection techniques MI, X 2, and more. The method is implemented and evaluated using several datasets. The experimental results show clearly that the method is effective in predicting the correct words by utilizing small contexts. The system achieved impressive results, compared with similar work; the accuracy in some experiments approaches 91 % correct predictions