Defining textual entailment

Abstract

Textual entailment is a relationship that obtains between fragments of text when one fragment in some sense implies the other fragment. The automation of textual entailment recognition supports a wide variety of textbased tasks, including information retrieval, information extraction, question answering, text summarization, and machine translation. Much ingenuity has been devoted to developing algorithms for identifying textual entailments, but relatively little to saying what textual entailment actually is. This article is a review of the logical and philosophical issues involved in providing an adequate definition of textual entailment. We show that many natural definitions of textual entailment are refuted by counterexamples, including the most widely cited definition of Dagan et al. We then articulate and defend the following revised definition: T textually entails H 5 df typically, a human reading T would be justified in inferring the proposition expressed by H from the proposition expressed by T. We also show that textual entailment is context-sensitive, nontransitive, and nonmonotonic.

Team GPLSI. Approach for automated fact checking

“…It is well known as an active research area in Natural Language Processing in the last decade. That is corroborated by the number of related papers (Korman, Mack, Jett, & Renear, 2018;Padó, Noh, Stern, Wang, & Zanoli, 2015;Paria, Annervaz, Dukkipati, Chatterjee, & Podder, 2016).…”

Natural Language Processing Applications: A New Taxonomy using Textual Entailment

“…USING ENTAILMENT Textual entailment is a relationship that obtains between fragments of text when one fragment in some sense implies the other fragment. The automation of textual entailment recognition supports a wide variety of text-based tasks, including information retrieval, information extraction, question answering, text summarization, and machine translation [2].…”

“…Textual entailment [1] is the process of importing a text from another one. Textual entailment [2] is an obtaining the relationship between fragments of text when one fragment in some sense implies the other.…”

“…Textual entailment research mainly contains methods for developing and evaluating algorithmic identification methods of entailment [2]. Text entailment aims at a deeper understanding of text and thinking, which shares the same type of machine learning comprehension, although the structure of the functions is slightly different [4].…”

“…Textual Entailment task is a problem of classification [5]. TE is the latest and best indicator of text acquisition in NLP applications [7].Recent work on Text Entailment work includes (1) models based on linked elements and machine-learning classifiers, (2) sentence-based models that use similar structures such as Semantic Textual similarity, sentence-based function, and (3) other neural network models that do not use Simple RRNs or CNNs as sentence encoders.…”