Results 21 to 30 of about 571 (258)
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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Recognizing Textual Entailment with a Semantic Edit Distance Metric
2012 11th Mexican International Conference on Artificial Intelligence, 2012 We present a Recognizing Textual Entailment(RTE) system based on different similarity metrics. The metricsused are string-based metrics and the Semantic Edit DistanceMetric, which is proposed in this paper to address limitationsof known semantic-based metrics and to support the decisionsmade by a simple method based on lexical similarity metrics.We add
MIGUEL Rios, Alexander Gelbukh
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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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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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A Cross-Lingual Hybrid Neural Network With Interaction Enhancement for Grading Short-Answer Texts
IEEE Access, 2023 Automatic Short-Answer Grading (ASAG) is an application for recognizing textual entailment in smart education. With the continuous expansion of the application scope of artificial neural networks, many deep learning models have been applied to grading ...
Yishan Chen +4 more
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Visual Denotations for Recognizing Textual Entailment [PDF]
Proceedings of the 2017 Conference on Empirical Methods in Natural
Language Processing, 2017 In the logic approach to Recognizing Textual Entailment, identifying phrase-to-phrase semantic relations is still an unsolved problem. Resources such as the Paraphrase Database offer limited coverage despite their large size whereas unsupervised distributional models of meaning often fail to recognize phrasal entailments.
Dan Han +2 more
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A semantic approach to recognizing textual entailment [PDF]
Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05, 2005 Exhaustive extraction of semantic information from text is one of the formidable goals of state-of-the-art NLP systems. In this paper, we take a step closer to this objective. We combine the semantic information provided by different resources and extract new semantic knowledge to improve the performance of a recognizing textual entailment system.
Marta Tatu, Dan I. Moldovan
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Inference rules for recognizing textual entailment [PDF]
Proceedings of the Eighth International Conference on Computational Semantics - IWCS-8 '09, 2009 In this paper, we explore the application of inference rules for recognizing textual entailment (RTE). We start with an automatically acquired collection and then propose methods to refine it and obtain more rules using a hand-crafted lexical resource.
Georgiana Dinu, Rui Wang 0005
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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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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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