Estimation in Semantic Similarity of Texts - Nguyen, Manh Hung; Tran, Dinh Que (Journal of Information Science and Engineering, 2021)
The semantic similarity of texts or documents has been widely studied in various areas including natural language processing, document comparison, artificial intelligence, semantic web, etc: Several similarity measures have been proposed but they are usually tied to special application domains or to data representation of various types. The purpose of this paper is to present a model for estimation in semantic similarity of texts based on similar sentences in structure of subjects, verbs and objects. And in turn, the semantic similarity of these components in the structure of sentences is estimated by means of the basic semantic similarity of words. The model is evaluated with two experiments: direct similarity and relative similarity among texts. The experimental results indicate that the proposed model is better than some baseline models in some circumstances.