Recognizing Textual Entailment

M. A. Muneer; D. Sairam; B. Nikhil; Ch. Shivani

Recognizing Textual Entailment

Authors: M. A. Muneer, D. Sairam, B. Nikhil, Ch. Shivani

Unique Paper ID: 155070
Volume: 8
Issue: 12
PageNo: 1371-1375

Keywords: Textual Entailment (TE) Natural Language Processing (NLP)

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.

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ISSN: 2349-6002
Volume: 8
Issue: 12
PageNo: 1371-1375

Recognizing Textual Entailment

Available:https://ijirt.org/Article?manuscript=155070

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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