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.