Measuring Semantic Textual Similarity of Sentences Using Modified Information Content and Lexical Taxonomy

摘要

In this paper, we present a survey and comparative studies on semantic textual similarity methods, those are based on WordNet taxonomy. We also proposed a new method for measuring semantic similarity between sentences. This proposed method, uses the advantages of taxonomy methods and merge these information to a language model. It considers the WordNet synsets for lexical relationships between nodes/words and uni-gram language model is implemented over a large corpus to assign the information content value between the two nodes of different classes. Finally, a similarity score is generated by considering the maximum weight and shortest distance of the graph. To evaluate and compare the method, SemEval 2015 English STS task 2 training dataset is considered.