A New Sentence Similarity Computing Technique Using Order and Semantic Similarity

Abstract

Techniques that detect sentence similarity have been a very important domain of research and lately many such techniques have been successfully implemented. With the use of Natural Language Processing (NLP) these techniques have been implemented more efficiently. The concept of semantic analysis is very significant in determining sentence similarity. The model proposed in this paper, deploys a NLP based methodology that works on the Sentence Involving Compositional Knowledge (SICK) dataset. The proposed methodology considers the set of sentencesto be a subset of words and it is split based on the semantic and syntactic structure. A lexical database is used by this model, unlike methods deployed by other models. This is followed by the computation of the word order vector. When this NLP based method is tested on the dataset, the accuracy obtained is 82.7% on the basis of mean absolute error. The obtained results are better than the previously used methods. Also, the proposed method is computationally faster than the existing methods.