Results 11 to 20 of about 368,749 (284)
Defining textual entailment [PDF]
Journal of the Association for Information Science and Technology, 2018 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 ...
Daniel Z. Korman +3 more
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Entailment Graph Learning with Textual Entailment and Soft Transitivity [PDF]
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022 Les graphiques d'implication typés essaient d'apprendre les relations d'implication entre les prédicats à partir du texte et de les modéliser en tant qu'arêtes entre les nœuds de prédicats. La construction des graphiques d'implication souffre généralement d'une grande rareté et d'un manque de fiabilité de la similitude distributionnelle. Nous proposons
Zhibin Chen, Yue Feng, Dong Liang Zhao
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Recognizing Partial Textual Entailment
, 2013 Textual entailment is an asymmetric relation between two text fragments that describes whether one fragment can be inferred from the other. It thus cannot capture the notion that the target fragment is “almost entailed” by the given text. The recently suggested idea of partial textual entailment may remedy this problem.
Levy, Omer +3 more
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Syntactic Testsuites and Textual Entailment Recognition [PDF]
Proceedings of the Language Resources and Evaluation Conference, 2010 We focus on textual entailments mediated by syntax and propose a new methodology to evaluate textual entailment recognition systems on such data. The main idea is to generate a syntactically annotated corpus of pairs of (non-)entailments and to use error mining to identify the most likely sources of errors.
Bedaride, Paul, Gardent, Claire
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Query-Based Extractive Multi-Document Summarization Using Paraphrasing and Textual Entailment [PDF]
مدیریت مهندسی و رایانش نرم, 2020 One of the most common problems with computer networks is the amount of information in these networks. Meanwhile searching and getting inform about content of textual document, as the most widespread forms of information on such networks, is difficult ...
Ali Naserasadi
doaj +1 more source
A study on textual entailment [PDF]
17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'05), 2005 In this paper we study a graph-based approach to the task of textual entailment between a text and hypothesis. The approach takes into account the full lexico-syntactic context of both the text and hypothesis and relies heavily on the concept of subsumption.
Vasile Rus +3 more
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Unsupervised Learning of Relational Entailment Graphs from Text [PDF]
, 2021 Recognizing textual entailment and paraphrasing is critical to many core natural language processing applications including question answering and semantic parsing.
Hosseini, Mohammad Javad
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Figurative Language in Recognizing Textual Entailment [PDF]
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, 2021 We introduce a collection of recognizing textual entailment (RTE) datasets focused on figurative language. We leverage five existing datasets annotated for a variety of figurative language -- simile, metaphor, and irony -- and frame them into over 12,500 RTE examples.We evaluate how well state-of-the-art models trained on popular RTE datasets capture ...
Tuhin Chakrabarty +3 more
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Asymmetric Attributional Word Similarity Measures to Detect the Relations of Textual Generality
Computers, 2020 In this work, we present a new unsupervised and language-independent methodology to detect the relations of textual generality. For this, we introduce a particular case of Textual Entailment (TE), namely Textual Entailment by Generality (TEG). TE aims to
Sebastião Pais, Gaël Dias
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Logic-Based Inference With Phrase Abduction Using Vision-and-Language Models
IEEE Access, 2023 Recognizing Textual Entailment (RTE) is among the most fundamental tasks in natural language processing applications, such as question answering and machine translation.
Akiyoshi Tomihari, Hitomi Yanaka
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