Recognizing Textual Entailment

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ISSN: 2349-6002 ESTD Year: 2014

UGC approved journal no 47859

Recognizing Textual Entailment
Author(s):
M. A. Muneer, D. Sairam, B. Nikhil, Ch. Shivani
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.
Article Details
Unique Paper ID: 155070 Publication Volume & Issue: Volume 8, Issue 12 Page(s): 1371 - 1375
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