My name is John Lawrence and I am a lecturer in the Centre for Argument Technology at the University of Dundee.

My research focuses include: argument mining, artificial intelligence, and computational linguistics.

You can read more about me, or view my publications and projects I have worked on.

Featured Projects

OVA: Online Visualisation of Argument



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Featured Publications

Argument Mining: A Survey

Lawrence, J., & Reed, C. (2019)

Computational Linguistics


Argument mining is the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in natural language. Understanding argumentative structure makes it possible to determine not only what positions people are adopting, but also why they hold the opinions they do, providing valuable insights in domains as diverse as financial market prediction and public relations. This survey explores the techniques that establish the foundations for argument mining, provides a review of recent advances in argument mining techniques, and discusses the challenges faced in automatically extracting a deeper understanding of reasoning expressed in language in general.

Lakatos-style collaborative mathematics through dialectical, structured and abstract argumentation

Pease, A., Lawrence, J., Budzynska, K., Corneli, J., & Reed, C. (2017)

Artificial Intelligence


The simulation of mathematical reasoning has been a driving force throughout the history of Artificial Intelligence research. However, despite significant successes in computer mathematics, computers are not widely used by mathematicians apart from their quotidian applications. An oft-cited reason for this is that current computational systems cannot do mathematics in the way that humans do. We draw on two areas in which Automated Theorem Proving (ATP) is currently unlike human mathematics: firstly in a focus on soundness, rather than understandability of proof, and secondly in social aspects. Employing techniques and tools from argumentation to build a framework for mixed-initiative collaboration, we develop three complementary arcs.

Multi-level computational methods for interdisciplinary research in the HathiTrust Digital Library

Murdock et al. (2017)



We show how faceted search using a combination of traditional classification systems and mixed-membership topic models can go beyond keyword search to inform resource discovery, hypothesis formulation, and argument extraction for interdisciplinary research. Our test domain is the history and philosophy of scientific work on animal mind and cognition. The methods can be generalized to other research areas and ultimately support a system for semi-automatic identification of argument structures. We provide a case study for the application of the methods to the problem of identifying and extracting arguments about anthropomorphism during a critical period in the development of comparative psychology.

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