Affiliations:
Department of Computer Science and Engineering, College of Engineering Trivandrum, (Affiliated to APJ Abdul Kalam Technological University), Thiruvananthapuram, Kerala, India
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Department of Computer Applications, College of Engineering Trivandrum, (Affiliated to APJ Abdul Kalam Technological University), Thiruvananthapuram, Kerala, India
Correspondence:
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Corresponding author. S. Nishy Reshmi, Department of Computer Science and Engineering, College of Engineering Trivandrum, (Affiliated to APJ Abdul Kalam Technological University), Thiruvananthapuram - 695016, Kerala, India. E-mail: [email protected].
Abstract: 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.
Keywords: GloVe, natural language processing, textual entailment, Wordnet
