Natural Language Processing Applications: A New Taxonomy using Textual Entailment

Elshazly, Manar; Haggag, Mohammed H.; Ehssan, Soha Ahmed

doi:10.14569/ijacsa.2021.0120580

Cited by 5 publications

(3 citation statements)

References 22 publications

(95 reference statements)

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“…By utilizing this analysis technique, it will be possible to summarize the information contained in large data tables down to some smaller summary index sets or appropriate groups. Research on the efficient detection of depression from textual social content was also carried out [65] researching a similar study which later found that textual is promising when used with deep learning that applies the GRU, Bi GRU, LSTM, Bi LSTM methods the result is an accuracy of 80 %, 81%, 80%, 81%. The testing process is divided into several stages.…”

Section: Methodsmentioning

confidence: 99%

Trends, Technology, and Implementation of Digital Counseling in a Human Mental Health

Permana

Hartati

Sudarma

et al. 2023

j. nas. pendidik. teknik. inform.

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This research discusses various trends, methods, and implementation of the art of digital-based counseling services. With increasing public awareness of mental health, since the womb, parents have thought about how to form good character and mental health for their children. Purpose of this study is to utilize digital counseling services to help early detection of depression and the resilience of children and adolescents in dealing with life's problems. This research collects articles according to the topic, then looks at cases handled, types of data, and methods used in both synchronous and asynchronous-based digital counseling. The next stage is research grouping based on research objects, data, methods, results, and deficiencies in research. The focus of the research is on cases of depression, and resilience, with machine learning methods. Digital counseling has been widely used for early detection in cases of depression, drug abuse, youth suicide, and alcohol addiction among adolescents

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Section: Methodsmentioning

confidence: 99%

Trends, Technology, and Implementation of Digital Counseling in a Human Mental Health

Permana

Hartati

Sudarma

et al. 2023

j. nas. pendidik. teknik. inform.

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“…-In most recent studies, e cient entailment models are often used in other elds such as medicine, cooking, and recognition of depression cases [21]; [22]. In fact, domain-speci c data is ne-tuned using BERT models.…”

Section: Contradiction Relationmentioning

confidence: 99%

A Knowledge-Based Approach for Recognizing Textual Entailments with a Focus on Causality and Contradiction

Rahimi,

ShamsFard

2024

Preprint

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There is usually implicit information in natural language texts that can be deduced from the text using background or common-sense knowledge. Recognition of textual entailments falls into this category. It involves receiving two pieces of text and deciding whether the second one entails or contradicts the first one, or if there is no such relation between the two. One of the types of entailment is causality, where the event or situation in one text can cause the event or situation in another text. The aim of this paper is to improve textual entailment by identifying causality and contradiction relationships between pieces of text using knowledge sources. To achieve this, we employ a knowledge-based method that recognizes textual entailments through the use of linguistic and common-sense knowledge (ontologies), with a particular emphasis on implicit or ambiguous causal and contradictory relationships. For this purpose, we first developed two ontologies, including causality and contradiction relations among concepts. Then, two concurrent paths in a pipeline are traversed. In the first path, a rule-based system is utilized to identify simple contradiction relations. The rules are extracted semi-automatically through data mining methods. In the second path, the provided knowledge of causality and contradiction is injected into language models using prompt engineering. Subsequently, the enriched language models undergo fine-tuning with entailment datasets to carry out textual entailment tasks. The results indicate that the combination of the rule-based method and enriched models with causality and contradiction knowledge yields the best performance. Our leading system demonstrates an accuracy of 87.5% on RTE tasks, trained and tested on the FarsTail dataset. This reflects a notable 3.5% improvement over baselines and state-of-the-art systems.

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“…p. ex. : Bernardy & Chatzikyriakidis, 2020 ;Crouch et al, 2005 ;Dagan et al, 2013 ;Elshazly, Haggag & Ehssan, 2021 ;Liu et al, 2020 ;Paramasivam & Nirmala, 2021).…”

Section: Introductionmentioning

confidence: 99%

Inférences textuelles et constructions à verbes supports

Banyś¹

2022

NEO

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Pour comprendre le langage naturel, les systèmes automatiques doivent avoir la capacité et la possibilité de savoir ce qui est inféré, et si oui, comment cela est inféré, et à partir de quoi dans un texte. Les implications linguistiquement déterminées imposées par un prédicat sur ses arguments propositionnels, apparaissant dans un texte comme des inférences textuelles, sont, avec d'autres éléments, tels que la connaissance du monde, les hypothèses des locuteurs sur l'utilisation de la langue, les stéréotypes situationnels, les implicatures, etc. l'un des éléments nécessaires pour qu'un système soit considéré comme comprenant le langage naturel. Dans cet article, nous nous concentrons sur l'une des parties importantes des inférences textuelles, qui n'a pratiquement pas été étudiée, à savoir les relations entre une catégorie particulière d'inférences textuelles constituée d'implicatifs phrastiques au sens de Karttunen (2012) et une catégorie particulière d'expressions polylexicales constituée de verbes supports. Nous donnons d'abord une brève introduction à l'état actuel de la description des deux catégories générales : les inférences, dont un type particulier d'inférences : les paraphrases (sec. 1), et les expressions polylexicales avec un type particulier de ces constructions : les verbes supports (sec. 2), ce qui nous permet de présenter les relations entre les verbes supports et les paraphrases (sec. 3). Nous passons ensuite à une discussion sur les implicatifs, y compris les implicatifs phrastiques (sec. 4), au sens de Karttunen (2012) et nous présentons une esquisse de la description de certains types de constructions à verbes supports du point de vue de leur pouvoir implicatif (sec. 5). Les analyses du type présenté sont à poursuivre et doivent être étendues à toutes les constructions du type analysé et font en même temps partie d'un projet beaucoup plus général. Elles sont un début d'une implémentation systématique à faire sur les matériaux français et polonais en corrélation avec l'anglais de la combinatoire des valeurs de vérité, "signatures implicatives", des prédicats, qu'ils soient verbaux, adjectivaux ou nominaux.

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Natural Language Processing Applications: A New Taxonomy using Textual Entailment

Cited by 5 publications

References 22 publications

Trends, Technology, and Implementation of Digital Counseling in a Human Mental Health

Trends, Technology, and Implementation of Digital Counseling in a Human Mental Health

A Knowledge-Based Approach for Recognizing Textual Entailments with a Focus on Causality and Contradiction

Inférences textuelles et constructions à verbes supports

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