Closing date: 02 December 2021
The Centre for Argument Technology currently has a vacancy in the rapidly expanding subfield of AI and natural language processing known as argument mining. A 48-month PhD studentship (at standard UKRI rates, currently £15,609 tax-free, plus payment of UK home fees) is available to explore the application of AI to understanding arguments and persuasion in financial discourse, and in corporate reporting in particular. The project is collaborative with a university in Switzerland which will provide complementary expertise in communication science. Further information and the details of this studentship are available on the website. For an informal discussion about the post, please contact Prof. Chris Reed via email: email@example.com.
The Centre for Argument Technology is a highly interdisciplinary environment, and candidates should be willing to work across traditional disciplinary boundaries. ARG-tech’s software stack provides an environment for academic tools (such as one of the most popular analysis tools for argumentation, OVA ova.arg.tech, and the only automated system for grading argument analysis, argugrader.com) as well as public-facing systems such as the Evidence Toolkit which was deployed into thousands of secondary schools in partnership with the BBC. We provide open infrastructure for datasets of argument and debate based on the AIF standard for argument representation and knowledge engineering. Most recently, we have also be working to provide an open framework for argument mining, one of the most demanding challenges in AI natural language processing today. The engineering of these tools and systems has been driven by theory coming from philosophy and linguistics but is aimed at translating basic research and delivering it robustly to large audiences. As a part of a research project funded by the Swiss National Science Foundation, we are looking to appoint a PhD student to work on argument mining and its role in financial communication. The proposed research is a large-scale study of argumentative patterns in a corpus of quarterly Earnings Conference Calls (ECCs), a key dialogical genre in the financial communication of listed companies.
Traditionally, the study of argumentation in context has relied on the analytical reconstruction of individual discourses examined in relation to descriptions of activity types in order to outline how the goals, incentives and procedures of activities constrain the issues and the material and procedural starting points of argumentative discussions. Recently, researchers have advocated a shift towards larger corpus studies not only to test and refine hypotheses on the contextual constraints on argumentation, but also to map inherently more complex networks of arguments in multi-party discussions that shape broader debates in society. This shift is also necessary in order to start addressing the effects of argumentation on the context itself. Corpus research on argumentation, however, requires the development of theory-based annotation schemes and time-consuming annotation by trained analysts, which risks being an insurmountable bottleneck. Furthermore, it requires quantitative analytics to relate analyzed arguments to contextual parameters and, if the effects of argumentation are to be addressed, the possibility to measure change in contextual parameters at appropriate points in time. Recourse to Argumentation Mining and related Argumentation Analytics appears promising, but tools and techniques developed in this growing field have so far seen limited application to research on discourse in context. The project seeks to demonstrate how these challenges can be met with an Argumentation Mining approach designed to investigate the interdependency of argumentation and activity type through the notion of argumentative pattern (AP), which refers to significant constellations of argumentative moves whose occurrence can be explained in view of the goals and rules of the activity type.
Essential knowledge, skills and experience
Desirable knowledge, skills and experience
This studentship includes a tax free stipend of £15,609 (plus payment of fees) for 48 months fixed term.
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