Argumentative Topology: Finding Loop(holes) in Logic
This work addresses the problem of identifying logical fallacies, specifically circular arguments, in text for NLP researchers, offering an incremental approach.
This paper explores the ability of word embeddings to capture the logical shape in text, specifically focusing on identifying circular arguments. It introduces a novel framework called Topological Word Embeddings, which uses dynamical system analysis and topological data analysis to extract shape-based notions of logic.
Advances in natural language processing have resulted in increased capabilities with respect to multiple tasks. One of the possible causes of the observed performance gains is the introduction of increasingly sophisticated text representations. While many of the new word embedding techniques can be shown to capture particular notions of sentiment or associative structures, we explore the ability of two different word embeddings to uncover or capture the notion of logical shape in text. To this end we present a novel framework that we call Topological Word Embeddings which leverages mathematical techniques in dynamical system analysis and data driven shape extraction (i.e. topological data analysis). In this preliminary work we show that using a topological delay embedding we are able to capture and extract a different, shape-based notion of logic aimed at answering the question "Can we find a circle in a circular argument?"