Multi-Task Learning for Semantic Relatedness and Textual Entailment
نویسندگان
چکیده
منابع مشابه
Recognizing Textual Entailment Using Description Logic and Semantic Relatedness
Recognizing Textual Entailment using Description Logic and Semantic Relatedness Reda Siblini, Ph.D. Concordia University, 2014 Textual entailment (TE) is a relation that holds between two pieces of text where one reading the first piece can conclude that the second is most likely true. Accurate approaches for textual entailment can be beneficial to various natural language processing (NLP) appl...
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We present a system for resolving both semantic relatedness (SR) and textual entailment (TE) tasks. There are two major contributions the method proposed here brings to the field:(1) it shows that there is a correlation between the SR scores and TE judgments which can be used to improve the accuracy of both of these tasks and (2) it shows that we can handle the structural information via patter...
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In this paper we present a novel technique for integrating lexical-semantic knowledge in systems for learning textual entailment recognition rules: the typed anchors. These describe the semantic relations between words across an entailment pair. We integrate our approach in the cross-pair similarity model. Experimental results show that our approach increases performance of cross-pair similarit...
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In this paper we present the creation of a corpora annotated with both semantic relatedness (SR) scores and textual entailment (TE) judgments. In building this corpus we aimed at discovering, if any, the relationship between these two tasks for the mutual benefit of resolving one of them by relying on the insights gained from the other. We considered a corpora already annotated with TE judgment...
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The problem of recognizing textual entailment (RTE) has been recently addressed using syntactic and lexical models with some success. Here, a new approach is taken to apply world knowledge in much the same way as humans, but captured in large semantic graphs such as WordNet. We show that semantic graphs made of synsets and selected relationships between them enable fairly simple methods that pr...
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ژورنال
عنوان ژورنال: Journal of Software Engineering and Applications
سال: 2019
ISSN: 1945-3116,1945-3124
DOI: 10.4236/jsea.2019.126012