Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{155070,
        author = {M. A. Muneer and D. Sairam and B. Nikhil and Ch. Shivani},
        title = {Recognizing Textual Entailment},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {12},
        pages = {1371-1375},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155070},
        abstract = {Textual Entailment methods recognizes pairs of natural language expressions such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. This simple abstraction of an exceedingly complex problem has broad appeal partly because it can be conceived also as a component in other NLP applications. If you have a system that is good at recognizing TE, it should be easier to build good systems for Information Retrieval (IR), Question Answering (QA), Paraphrase Recognition (PR), and Information Extraction (IE) and Summarization. Typically, entailment is used as part of a large system.},
        keywords = {Textual Entailment (TE), Natural Language Processing (NLP)},
        month = {},
        }