RT Journal Article
T1 IAT/ML: a metamodel and modelling approach for discourse analysis
A1 González Pérez, César
A1 Pereira Fariña, Martín
A1 Calderón Cerrato, Beatriz
A1 Martín Rodilla, Patricia
K1 Natural language
K1 Discourse
K1 Argumentation
K1 Ontologies
K1 Metamodel
K1 IAT/ML
AB 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.
PB Springer
SN 1619-1366
YR 2024
FD 2024-09-11
LK https://hdl.handle.net/10347/42835
UL https://hdl.handle.net/10347/42835
LA eng
NO Gonzalez-Perez, C., Pereira-Fariña, M., Calderón-Cerrato, B. et al. IAT/ML: a metamodel and modelling approach for discourse analysis. Softw Syst Model 23, 1157–1181 (2024). https://doi.org/10.1007/s10270-024-01208-7
NO The work presented in this paper has been partially funded by the AEI (Spanish National Research Agency) through grants PID2020-114758RB-I00, MCIN/AEI/https://doi.org/10.13039/ 501100011033 and PID2020-115482GB-I00, MCIN/AEI/https://doi. org/10.13039/501100011033.
NO Funding Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.
DS Minerva
RD 29 abr 2026