Integrated Subject–Action–Object and Bayesian Models of Intelligent Word Semantic Similarity Measures

Synonym similarity judgments based on semantic distance calculation play a vital role in supporting applications in the field of Natural Language Processing (NLP). However, existing semantic computing methods excessively rely on low-efficiency human supervision or high-quality datasets, which limits their further application. For these reasons, this paper proposes an automatic and intelligent method for calculating semantic similarity that integrates Subject–Action–Object (SAO) and WordNet to combine knowledge-based semantic similarity and corpus-based semantic similarity. First, the SAO structure is extracted from the Wikipedia dataset, and the statistics of SAO similarity are obtained by calculating co-occurrences of words in SAO. Second, the semantic similarity parameters of words are obtained based on WordNet, and the semantic similarity parameters are adjusted by Laplace Smoothing (LS). Finally, the semantic similarity can be obtained by the Bayesian Model (BM), which combines the semantic similarity parameter and the SAO similarity statistics. The experimental results from well-known word similarity datasets show that the proposed method outperforms traditional methods and even Large Language Models (LLM) in terms of accuracy. The Pearson, Spearman, and Kendall indices were introduced to prove the superiority of the proposed algorithm between model scores and human judgements.