IAT/ML: A Domain-Specific Approach for Discourse Analysis and Processing

Language technologies are gaining momentum as textual information saturate social networks and media outlets, compounded by the growing role of fake news and disinformation. In this context, approaches to represent and analyse 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 complex and highly context-dependent problems of discourse evaluation that society demands. In this paper, we present IAT/ML, a modelling approach to represent and analyse discourses. IAT/ML focus on semantic and pragmatic issues, thus tackling a little researched area in language technologies. It does so by combining three analysis approaches: ontological, which focuses on what the discourse talks about, argumentation, which deals with how the text justifies what it says, and critical, which provides insights into the speakers' beliefs and intentions. Together, these three modelling and analysis approaches make IAT/ML a comprehensive solution to represent and analyse complex discourses towards their evaluation and fact checking.

Domain-specific modelling. Discourse. Argumentation. Ontologies. IAT/ML.