IAT/ML: a metamodel and modelling approach for discourse analysis

IF 2 3区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Software and Systems Modeling Pub Date : 2024-09-11 DOI:10.1007/s10270-024-01208-7
Cesar Gonzalez-Perez, Martín Pereira-Fariña, Beatriz Calderón-Cerrato, Patricia Martín-Rodilla
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Abstract

Language technologies are gaining momentum as textual information saturates social networks and media outlets, compounded by the growing role of fake news and disinformation. In this context, approaches to represent and analyse public speeches, news releases, social media posts and other types of discourses are becoming crucial. Although there is a large body of literature on text-based machine learning, it tends to focus on lexical and syntactical issues rather than semantic or pragmatic. Being useful, these advances cannot tackle the nuanced and highly context-dependent problems of discourse evaluation that society demands. In this paper, we present IAT/ML, a metamodel and modelling approach to represent and analyse discourses. IAT/ML focuses on semantic and pragmatic issues, thus tackling a little researched area in language technologies. It does so by combining three different modelling approaches: ontological, which focuses on what the discourse is about; argumentation, which deals with how the text justifies what it says; and agency, which provides insights into the speakers’ beliefs, desires and intentions. Together, these three modelling approaches make IAT/ML a comprehensive solution to represent and analyse complex discourses towards their understanding, evaluation and fact checking.

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IAT/ML:话语分析的元模型和建模方法
随着文本信息充斥社交网络和媒体渠道,再加上假新闻和虚假信息的作用越来越大,语言技术的发展势头日益强劲。在这种情况下,表示和分析公开演讲、新闻稿、社交媒体帖子和其他类型话语的方法变得至关重要。虽然有大量关于基于文本的机器学习的文献,但这些文献往往侧重于词汇和句法问题,而不是语义或语用问题。这些进展虽然有用,但无法解决社会所需的细微且高度依赖语境的话语评估问题。在本文中,我们介绍了 IAT/ML,一种用于表示和分析话语的元模型和建模方法。IAT/ML 专注于语义和语用问题,从而解决了语言技术中一个鲜有研究的领域。为此,它结合了三种不同的建模方法:本体论方法,侧重于话语的内容;论证方法,处理文本如何证明其所说内容的合理性;代理方法,提供对说话者的信念、愿望和意图的洞察。这三种建模方法结合在一起,使 IAT/ML 成为一种全面的解决方案,可用于表示和分析复杂的话语,从而对其进行理解、评估和事实核查。
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来源期刊
Software and Systems Modeling
Software and Systems Modeling 工程技术-计算机:软件工程
CiteScore
6.00
自引率
20.00%
发文量
104
审稿时长
>12 weeks
期刊介绍: We invite authors to submit papers that discuss and analyze research challenges and experiences pertaining to software and system modeling languages, techniques, tools, practices and other facets. The following are some of the topic areas that are of special interest, but the journal publishes on a wide range of software and systems modeling concerns: Domain-specific models and modeling standards; Model-based testing techniques; Model-based simulation techniques; Formal syntax and semantics of modeling languages such as the UML; Rigorous model-based analysis; Model composition, refinement and transformation; Software Language Engineering; Modeling Languages in Science and Engineering; Language Adaptation and Composition; Metamodeling techniques; Measuring quality of models and languages; Ontological approaches to model engineering; Generating test and code artifacts from models; Model synthesis; Methodology; Model development tool environments; Modeling Cyberphysical Systems; Data intensive modeling; Derivation of explicit models from data; Case studies and experience reports with significant modeling lessons learned; Comparative analyses of modeling languages and techniques; Scientific assessment of modeling practices
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