Research on Computational Pragmatics Model Based on Improved Bayesian Algorithm

Conversational implicature is the core concept of pragmatics, which is of great significance to natural language processing (NLP). However, the current intelligent systems are insufficient in dealing with conversational implicature, especially the new generation of systems based on deep learning and data-driven. Due to the failure to fully integrate pragmatic theories, the depth and accuracy of semantic understanding are limited. Therefore, this paper models the expression and reasoning process of conversational implicature based on Bayesian theory. In the feature extraction stage, Normalized Google Distance (NGD) is introduced to measure the semantic correlation between words, and the node weights are recalculated. The key features are extracted by the improved NGD-TextRank algorithm, and the redundant attributes are removed to reduce the dimension. In the classification process, the influence degree of different feature items is divided, and their weights are integrated into the naive Bayes formula to construct a weighted naive Bayes classification algorithm. The research results show that the model can effectively solve the problems of pragmatics in conversational implicature, and provide new theoretical support and technical path for NLP and related fields.