Conceptual Analysis in a computer-assisted framework: mind in Peirce

Abstract

Conceptual Analysis (CA) is a matter-of-course practice for philosophers and other scholars in the humanities. Exploring one author’s corpus of texts in order to discover the various properties of a concept is a classic example of CA. Recently, a corpus-based computational framework for CA has been emerging in response to the methodological challenges brought about by the massive digitization of texts. In this framework, CA is approached by implementing a computer-assisted text analysis method, within which algorithms are used to support the various cognitive operations involved in CA. In this article, we focus on the retrieval of relevant text segments for analysis. However, this is a complex issue within a computational framework, since the relation between concept and natural language depends on several semantic phenomena, including synonymy, polysemy, and contextual modulation. The main contribution of this article is methodological because it explores the computational approach to CA. We present three algorithmic methods, which identify relevant text segments while taking into account various semantic phenomena. The results show the potential of computer-assisted CA, thereby highlighting the need to overcome the limitations of these first experiments. An additional contribution of this work takes the form of knowledge transfer from Artificial Intelligence to the Humanities.

Keywords

• Conceptual analysis
• Text mining
• Machine learning
• Mind
• Peirce
• Philosophy