Resources


The following technical documentation and training materials are related to IAT/ML version 1.0.2.

Technical documentation


IAT/ML White Paper
8 May 2024
This document provides a brief overview of IAT/ML, which may be useful when evaluating its adoption.
IAT/ML Analysis Process Guidelines
8 May 2024
This document provides a comprehensive process-oriented description of how to use IAT/ML.
IAT/ML Release Notes
8 May 2024
This document provides a description of the previous, current and planned versions of IAT/ML.

IAT/ML Ontology Patterns Guidelines
8 May 2024
This document provides a catalogue of ontology patterns that you may find, plus guidance on how to address each when using IAT/ML.
IAT/ML Argumentation Patterns Guidelines
8 May 2024
This document provides a catalogue of argumentation patterns that you may find, plus guidance on how to address each when using IAT/ML.
IAT/ML Agency Patterns Guidelines
8 May 2024
This document provides a catalogue of agency patterns that you may find, plus guidance on how to address each when using IAT/ML.

IAT/ML Technical Specification
8 May 2024
This document provides a detailed description of IAT/ML that can be used as a reference.
IAT/ML Metamodel
8 May 2024
This Bundt model file contains a formal specification of the IAT/ML metamodel, which you can open and view by using Bundt.


Training materials


IAT/ML Basic Graphical Notation Summary
8 May 2024
This document provides a quick reference to the IAT/ML notation, which you can use as a cheat sheet.


Research papers


IAT/ML: A Metamodel and Modelling Approach for Discourse Analysis
Cesar Gonzalez-Perez, Martín Pereira-Fariña, Beatriz Calderón-Cerrato & Patricia Martín-Rodilla, 2024
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.

Connecting Discourse and Domain Models in Discourse Analysis through Ontological Proxies
Cesar Gonzalez-Perez, 2020
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.