Recursos

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.

Argumentation-oriented discourse analysis usually focuses on what is being said and how, following the text under analysis quite literally, and paying little attention to the things in the world to which the text refers. However, to perform argumentation-oriented discourse analysis, one must assume certain conceptualisations by the speaker in order to interpret and reconstruct propositions and argumentation structures. These conceptualisations are rarely captured as a product of the analysis process. In this paper, we argue that considering the ontology to which a discourse refers as well as the text itself provides a richer and more useful representation of the discourse and its argumentation structures, facilitates intertextual analysis, and improves understandability of the analysis products. To this end, we propose the notion of ontological proxies, i.e., conceptual artefacts that connect elements in the argumentation structure to the associated ontology elements.