Results 21 to 30 of about 631,702 (254)
A mostly unlexicalized model for recognizing textual entailment [PDF]
Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), 2018 Many approaches to automatically recognizing entailment relations have employed classifiers over hand engineered lexicalized features, or deep learning models that implicitly capture lexicalization through word embeddings. This reliance on lexicalization may complicate the adaptation of these tools between domains. For example, such a system trained in
Mithun Paul +2 more
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From lexical entailment to recognizing textual entailment using linguistic resources [PDF]
, 2009 From lexical entailment to recognizing textual entailment using linguistic ...
Bahadorreza Ofoghi (13059933) +1 more
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Dependency-based paraphrasing for recognizing textual entailment [PDF]
Proceedings of the ACL-PASCAL Workshop on Textual Entailment and Paraphrasing - RTE '07, 2007 In this article we address the usefulness of linguistic-independent methods in extractive Automatic Summarization, arguing that linguistic knowledge is not only useful, but may be necessary to improve the informativeness of automatic extracts. An assessment of four diverse AS methods on Brazilian Portuguese texts is presented to support our claim.
Marsi, E.C., Krahmer, E.J., Bosma, W.E.
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A transformation-driven approach for recognizing textual entailment [PDF]
Natural Language Engineering, 2016 AbstractTextual Entailment is a directional relation between two text fragments. The relation holds whenever the truth of one text fragment, called Hypothesis (H), follows from another text fragment, called Text (T). Up until now, using machine learning approaches for recognizing textual entailment has been hampered by the limited availability of data.
Zanoli, Roberto, Colombo, Silvia
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Learning to recognize features of valid textual entailments [PDF]
Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics -, 2006 This paper advocates a new architecture for textual inference in which finding a good alignment is separated from evaluating entailment. Current approaches to semantic inference in question answering and textual entailment have approximated the entailment problem as that of computing the best alignment of the hypothesis to the text, using a locally ...
Bill MacCartney +4 more
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Recognizing Textual Entailment with Statistical Methods [PDF]
, 2010 In this paper we propose a new cause-effect non-symmetric measure applied to the task of Recognizing Textual Entailment. First we searched over a big corpus for sentences which contains the discourse marker "because" and collected cause-effect pairs.
Miguel Ángel Ríos-Gaona +2 more
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Inherent Disagreements in Human Textual Inferences
Transactions of the Association for Computational Linguistics, 2019 We analyze human’s disagreements about the validity of natural language inferences. We show that, very often, disagreements are not dismissible as annotation “noise”, but rather persist as we collect more ratings and as we vary the amount of context ...
Pavlick, Ellie, Kwiatkowski, Tom
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A question-entailment approach to question answering
BMC Bioinformatics, 2019 Background One of the challenges in large-scale information retrieval (IR) is developing fine-grained and domain-specific methods to answer natural language questions.
Asma Ben Abacha, Dina Demner-Fushman
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Recognizing Textual Entailment Using Probabilistic Inference [PDF]
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015 Reconocer Text Entailment (RTE) juega un papel importante en las aplicaciones de PNL, incluidas las respuestas a preguntas, la recuperación de información, etc. En trabajos recientes, algunas investigaciones exploran expresiones "profundas" como los compromisos discursivos o la lógica estricta para representar el texto.
Lei Sha +4 more
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Using discourse commitments to recognize textual entailment [PDF]
Proceedings of the 22nd International Conference on Computational Linguistics - COLING '08, 2008 In this paper, we introduce a new framework for recognizing textual entailment (RTE) which depends on extraction of the set of publicly-held beliefs -- known as discourse commitments -- that can be ascribed to the author of a text (t) or a hypothesis (h).
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