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Ontological Foundations for Scholarly Debate Mapping Technology
Mapping scholarly debates is an important genre of what can be called Knowledge Domain Analytics (KDA) technology – i.e. technology which combines both quantitative and qualitative methods of analysing specialist knowledge domains. However, current KDA technology research has emerged from diverse traditions and thus lacks a common conceptual foundation. This paper reports on the design of a KDA ontology that aims to provide this foundation. The paper then describes the argumentation extensions to the ontology for supporting scholarly debate mapping as a special form of KDA and demonstrates its expressive capabilities using a case study debate
Analysis of Dialogical Argumentation via Finite State Machines
Dialogical argumentation is an important cognitive activity by which agents
exchange arguments and counterarguments as part of some process such as
discussion, debate, persuasion and negotiation. Whilst numerous formal systems
have been proposed, there is a lack of frameworks for implementing and
evaluating these proposals. First-order executable logic has been proposed as a
general framework for specifying and analysing dialogical argumentation. In
this paper, we investigate how we can implement systems for dialogical
argumentation using propositional executable logic. Our approach is to present
and evaluate an algorithm that generates a finite state machine that reflects a
propositional executable logic specification for a dialogical argumentation
together with an initial state. We also consider how the finite state machines
can be analysed, with the minimax strategy being used as an illustration of the
kinds of empirical analysis that can be undertaken.Comment: 10 page
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Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL
Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N
Introduction
What is critical thinking, especially in the context of higher education?
How have research and scholarship on the matter developed over recent past
decades? What is the current state of the art here? How might the potential of
critical thinking be enhanced? What kinds of teaching are necessary in order
to realize that potential? And just why is this topic important now? These are
the key questions motivating this volume. We hesitate to use terms such as
“comprehensive” or “complete” or “definitive,” but we believe that, taken in
the round, the chapters in this volume together offer a fair insight into the
contemporary understandings of higher education worldwide. We also believe
that this volume is much needed, and we shall try to justify that claim in this
introduction
Argumentation Mining in User-Generated Web Discourse
The goal of argumentation mining, an evolving research field in computational
linguistics, is to design methods capable of analyzing people's argumentation.
In this article, we go beyond the state of the art in several ways. (i) We deal
with actual Web data and take up the challenges given by the variety of
registers, multiple domains, and unrestricted noisy user-generated Web
discourse. (ii) We bridge the gap between normative argumentation theories and
argumentation phenomena encountered in actual data by adapting an argumentation
model tested in an extensive annotation study. (iii) We create a new gold
standard corpus (90k tokens in 340 documents) and experiment with several
machine learning methods to identify argument components. We offer the data,
source codes, and annotation guidelines to the community under free licenses.
Our findings show that argumentation mining in user-generated Web discourse is
a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in
User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17
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