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Asymmetric Attributional Word Similarity Measures to Detect the Relations of Textual Generality

by 1,2,3,*,† and 2,†
1
Departamento de Informática, Faculdade de Engenharia, Universidade da Beira Interior, 6201-001 Covilhã, Portugal
2
Groupe de Recherche en Informatique, Automatique, Image et Instrumentation (GREYC), National Graduate School of Engineering and Research Center (ENSICAEN), Université de Caen Normandie (UNICAEN), 14000 Caen, France
3
NOVA Laboratory for Computer Science and Informatics, Departamento de Informática, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 1099-085 Lisboa, Portugal
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Computers 2020, 9(4), 81; https://doi.org/10.3390/computers9040081
Received: 7 August 2020 / Revised: 29 September 2020 / Accepted: 29 September 2020 / Published: 10 October 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 capture primary semantic inference needs across applications in Natural Language Processing (NLP). Since 2005, in the TE Recognition (RTE) task, systems have been asked to automatically judge whether the meaning of a portion of the text, the Text (T), entails the meaning of another text, the Hypothesis (H). Several novel approaches and improvements in TE technologies demonstrated in RTE Challenges are signaling renewed interest towards a more in-depth and better understanding of the core phenomena involved in TE. In line with this direction, in this work, we focus on a particular case of entailment, entailment by generality, to detect the relations of textual generality. In text, there are different kinds of entailments, yielded from different types of implicative reasoning (lexical, syntactical, common sense based), but here, we focus just on TEG, which can be defined as an entailment from a specific statement towards a relatively more general one. Therefore, we have TGH whenever the premise T entails the hypothesis H, this also being more general than the premise. We propose an unsupervised and language-independent method to recognize TEGs, from a pair T,H having an entailment relation. To this end, we introduce an Informative Asymmetric Measure (IAM) called Simplified Asymmetric InfoSimba (AISs), which we combine with different Asymmetric Association Measures (AAM). In this work, we hypothesize about the existence of a particular mode of TE, namely TEG. Thus, the main contribution of our study is highlighting the importance of this inference mechanism. Consequently, the new annotation data seem to be a valuable resource for the community. View Full-Text
Keywords: textual entailment by generality; asymmetric word similarities; asymmetric association measure; informative asymmetric measure textual entailment by generality; asymmetric word similarities; asymmetric association measure; informative asymmetric measure
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MDPI and ACS Style

Pais, S.; Dias, G. Asymmetric Attributional Word Similarity Measures to Detect the Relations of Textual Generality. Computers 2020, 9, 81. https://doi.org/10.3390/computers9040081

AMA Style

Pais S, Dias G. Asymmetric Attributional Word Similarity Measures to Detect the Relations of Textual Generality. Computers. 2020; 9(4):81. https://doi.org/10.3390/computers9040081

Chicago/Turabian Style

Pais, Sebastião, and Gaël Dias. 2020. "Asymmetric Attributional Word Similarity Measures to Detect the Relations of Textual Generality" Computers 9, no. 4: 81. https://doi.org/10.3390/computers9040081

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