In computer science, textual similarity used for detecting the similarity between words, terms, sentences, paragraph, and document. In natural language processing, sentence similarity performs the tasks such as document summarization, word sense disambiguation, short answer grading , and information retrieval. The lexical overlapping approach evaluates the similarity between the sentence and finds whether a sentence pair is semantically equivalent or not. Existing methods are used for checking the similarity of long text documents. These methods process sentences in high-dimensional space and are not much efficient, requires human input and also not adaptable to some application domains. Semantic textual similarity methods improved in two areas -(a) in the semantic relation between the words and (b) in semantic resources to reduce the dimension. The proposed architecture uses the two methods for directly computing the similarity between very short texts of the sentence and long text sentences. The Weighted Overlap Approach based proposed method provides a nonparametric similarity by comparing the similarity of the rankings for an intersection of the senses in both the sentences. The Cosine similarity based proposed method identify all distinct words from the sentences. In the proposed work the similarity detection methods are focused to check the synonyms similarity between the sentences.

Natural Language Processing, Semantic Textual Similarity, Word Similarity, Sentence similarity, Text Similarity

[1]. Y. Li, D. McLean, Z. A. Bandar, J. D. OShea, and K. Crockett. 2006. "Sentence similarity based on semantic nets and corpus statistics". In the procedding of Transactions on Knowledge and Data Engineering IEEE, Vol.18(8), pp. 1138-1150.
[2]. Y. Liu and C.Q. Zong, "Example-Based Chinese-English Machine Translation", In the proceding of.2004 IEEE Intternational Conf Systems, Man, and Cybernetics, Vol.1-7, 2004, pp.6093-6096.
[3]. LiHong, D. Wang, and M. Huang, "Improved Sentence Similarity Algorithm based on VSM and its application in Question Answering System". Intelligent Computing and Intelligent Systems (ICIS), 2010, In the procedding of International Conference on IEEE, 2010, pp. 368 - 371.
[4]. Eneko Agirre, Daniel Cer, Mona Diab and Gonzalez-Agirre Aitore, 2012. SemEval-2012 task6: "A pilot on Semantic textual Similarity". procedding of First Joint Conference on Lexical and Computational Semanticcs,June 7-8, 2012. pages 385-393.
[5]. Z. Wu and M. Palmer, "Verb semantics and lexical selection", In the Proceedings of 32nd annual Meeting of the Association for Computational Linguistics, (1994) June. IEEE, 2005, pp. 27-30.
[6]. R. M. Courtney Corley, "Measuring the semantic similarity of texts," proceding of in ACL workshop on Empirical Modeling of semantic Equivalence and Entailment (EMSEE),2013. IEEE, 005, pp. 13-18.
[7]. R. RL. Xu, D. Wang, and M. Huang, "Improved Sentence Similarity Algorithm based on VSM and its application in Question Answering System." Intelligent Computing and Intelligent Systems (ICIS), 2010, procedding of International Conference on IEEE, 2010, pp. 368 - 371.
[8]. Jiang, Jay J., and David W. Conrath. "Semantic similarity based on corpus statistics and lexical taxonomy", In the Proceedings of ROCLING X, Taiwan, 1997, https://arxiv.org/abs/cmp-lg/9709008 accessed on November 7, 2017.
[9]. Z.Jingling , Z. Huiyun , Cui .Baojiang ."Sentence Similarity Based on Semantic Vector Model" In the procedding of Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing 2014, IEEE, pp. 499-503.
[10]. Emiliano Giovannetti, Simone Marchi, and Simonetta Montemagni, "Combining Statistical Techniques and Lexico -syntactic Patterns for Semantic Relations Extraction from Text", In the procedding of sixth international conference on Stastical Technique 2008,IEEE, pp. 399-402.