{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T18:18:11Z","timestamp":1769710691574,"version":"3.49.0"},"reference-count":30,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,7,2]]},"abstract":"<jats:p>In this paper, we propose a method exploiting syntactic structure, semantic relations and word embeddings for recognizing textual entailment. The sentence pairs are analyzed using their syntactic structure and categorization of sentences in active voice, sentences in passive voice and sentences holding copular relations. The main syntactic relations such as subject, verb and object are extracted and lemmatized using a lemmatization algorithm based on parts-of-speech. The subject-to-subject, verb-to-verb and object-to-object similarity is identified using enhanced Wordnet semantic relations. Further similarity is analyzed using modifier relation, number relation, nominal modifier relation, compound relation, conjunction relation and negative relation. The experimental evaluation of the method on Stanford Natural Language Inference dataset shows that the accuracy of the method is 1.4% more when compared to the state-of-the-art zero shot domain adaptation methods.<\/jats:p>","DOI":"10.3233\/jifs-223275","type":"journal-article","created":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T12:26:25Z","timestamp":1682425585000},"page":"929-939","source":"Crossref","is-referenced-by-count":2,"title":["Textual entailment classification using syntactic structures and semantic relations"],"prefix":"10.1177","volume":"45","author":[{"given":"S.","family":"Nishy Reshmi","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, College of Engineering Trivandrum, (Affiliated to APJ Abdul Kalam Technological University), Thiruvananthapuram, Kerala, India"}]},{"given":"R.","family":"Shreelekshmi","sequence":"additional","affiliation":[{"name":"Department of Computer Applications, College of Engineering Trivandrum, (Affiliated to APJ Abdul Kalam Technological University), Thiruvananthapuram, Kerala, India"}]}],"member":"179","reference":[{"issue":"04","key":"10.3233\/JIFS-223275_ref1","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1142\/S0218213008004096","article-title":"A study of textual entailment","volume":"17","author":"Rus","year":"2008","journal-title":"International Journal on Artificial Intelligence Tools"},{"key":"10.3233\/JIFS-223275_ref2","first-page":"177","volume-title":"Machine Learning Challenges Workshop","author":"Dagan"},{"issue":"4","key":"10.3233\/JIFS-223275_ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2382593.2382599","article-title":"Validating contradiction in texts using online comention pattern checking","volume":"11","author":"Shih","year":"2012","journal-title":"ACM Transactions on Asian Language Information Processing (TALIP)"},{"key":"10.3233\/JIFS-223275_ref4","doi-asserted-by":"crossref","first-page":"37","DOI":"10.3115\/1631862.1631869","article-title":"Discovering entailment relations using \u201ctextual entailment patterns\u201d","author":"Zanzotto","year":"2005","journal-title":"Proceedings of the ACL Workshop on Empirical Modeling of Semantic Equivalence and Entailment"},{"key":"10.3233\/JIFS-223275_ref5","first-page":"86","article-title":"Contradiction-focused qualitative evaluation of textual entailment","author":"Magnini","year":"2010","journal-title":"Proceedings of the Workshop on Negation and Speculation in Natural Language Processing"},{"key":"10.3233\/JIFS-223275_ref6","first-page":"784","article-title":"Recognizing textual relatedness with predicate-argument structures","author":"Wang","year":"2009","journal-title":"Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing"},{"key":"10.3233\/JIFS-223275_ref7","unstructured":"Tsuchiya Masatoshi, Performance impact caused by hidden bias of training data for recognizing textual entailment, In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) Miyazaki, Japan, May 2018. 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