AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This keynote talk presents a key challenge which conjugate argument mining with formal computational models of argumentation, i.e., the assessment of the trustworthiness of natural language arguments, with a focus on fallacious argumentation, with the aim to show how these methods can be used to automatically identify fallacious arguments in political debates.
Global practice in computational linguistics demonstrates how machines can automatically find, extract, and understand arguments in texts. In the field of natural language, automated argumentation analysis is becoming increasingly important. Existing research mostly covers languages with welldeveloped resource bases, s...
Several opinion pieces published in the media have positioned themselves for or against the impeachment process of former Brazilian president Dilma Rousseff in 2016. The debate of these controversial discourses about the impeachment was realized in language through lexicogrammatical resources to connect reasoning and c...
Large language models are moving from helping write up research to helping do it, which makes it important to know how the scientific text they produce differs from human writing. Work on this question has stayed mostly at the surface, using lexical and stylistic cues that light paraphrasing erases. We look instead at...
Argument Mining (AM) transforms natural language into its underlying argument structures. This transformation is typically realized through a sequence of AM tasks that form an end-to-end AM pipeline. However, AM approaches often differ in how they conceptualize these tasks, making direct comparisons between them diffic...
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