14,981 research outputs found
Recognizing cited facts and principles in legal judgements
In common law jurisdictions, legal professionals cite facts and legal principles from precedent cases to support their arguments before the court for their intended outcome in a current case. This practice stems from the doctrine of stare decisis, where cases that have similar facts should receive similar decisions with respect to the principles. It is essential for legal professionals to identify such facts and principles in precedent cases, though this is a highly time intensive task. In this paper, we present studies that demonstrate that human annotators can achieve reasonable agreement on which sentences in legal judgements contain cited facts and principles (respectively, Îș=0.65 and Îș=0.95 for inter- and intra-annotator agreement). We further demonstrate that it is feasible to automatically annotate sentences containing such legal facts and principles in a supervised machine learning framework based on linguistic features, reporting per category precision and recall figures of between 0.79 and 0.89 for classifying sentences in legal judgements as cited facts, principles or neither using a Bayesian classifier, with an overall Îș of 0.72 with the human-annotated gold standard
Analytic frameworks for assessing dialogic argumentation in online learning environments
Over the last decade, researchers have developed sophisticated online learning environments to support students engaging in argumentation. This review first considers the range of functionalities incorporated within these online environments. The review then presents five categories of analytic frameworks focusing on (1) formal argumentation structure, (2) normative quality, (3) nature and function of contributions within the dialog, (4) epistemic nature of reasoning, and (5) patterns and trajectories of participant interaction. Example analytic frameworks from each category are presented in detail rich enough to illustrate their nature and structure. This rich detail is intended to facilitate researchersâ identification of possible frameworks to draw upon in developing or adopting analytic methods for their own work. Each framework is applied to a shared segment of student dialog to facilitate this illustration and comparison process. Synthetic discussions of each category consider the frameworks in light of the underlying theoretical perspectives on argumentation, pedagogical goals, and online environmental structures. Ultimately the review underscores the diversity of perspectives represented in this research, the importance of clearly specifying theoretical and environmental commitments throughout the process of developing or adopting an analytic framework, and the role of analytic frameworks in the future development of online learning environments for argumentation
Dispute Resolution Using Argumentation-Based Mediation
Mediation is a process, in which both parties agree to resolve their dispute
by negotiating over alternative solutions presented by a mediator. In order to
construct such solutions, mediation brings more information and knowledge, and,
if possible, resources to the negotiation table. The contribution of this paper
is the automated mediation machinery which does that. It presents an
argumentation-based mediation approach that extends the logic-based approach to
argumentation-based negotiation involving BDI agents. The paper describes the
mediation algorithm. For comparison it illustrates the method with a case study
used in an earlier work. It demonstrates how the computational mediator can
deal with realistic situations in which the negotiating agents would otherwise
fail due to lack of knowledge and/or resources.Comment: 6 page
An Argumentation-Based Reasoner to Assist Digital Investigation and Attribution of Cyber-Attacks
We expect an increase in the frequency and severity of cyber-attacks that
comes along with the need for efficient security countermeasures. The process
of attributing a cyber-attack helps to construct efficient and targeted
mitigating and preventive security measures. In this work, we propose an
argumentation-based reasoner (ABR) as a proof-of-concept tool that can help a
forensics analyst during the analysis of forensic evidence and the attribution
process. Given the evidence collected from a cyber-attack, our reasoner can
assist the analyst during the investigation process, by helping him/her to
analyze the evidence and identify who performed the attack. Furthermore, it
suggests to the analyst where to focus further analyses by giving hints of the
missing evidence or new investigation paths to follow. ABR is the first
automatic reasoner that can combine both technical and social evidence in the
analysis of a cyber-attack, and that can also cope with incomplete and
conflicting information. To illustrate how ABR can assist in the analysis and
attribution of cyber-attacks we have used examples of cyber-attacks and their
analyses as reported in publicly available reports and online literature. We do
not mean to either agree or disagree with the analyses presented therein or
reach attribution conclusions
Argotario: Computational Argumentation Meets Serious Games
An important skill in critical thinking and argumentation is the ability to
spot and recognize fallacies. Fallacious arguments, omnipresent in
argumentative discourse, can be deceptive, manipulative, or simply leading to
`wrong moves' in a discussion. Despite their importance, argumentation scholars
and NLP researchers with focus on argumentation quality have not yet
investigated fallacies empirically. The nonexistence of resources dealing with
fallacious argumentation calls for scalable approaches to data acquisition and
annotation, for which the serious games methodology offers an appealing, yet
unexplored, alternative. We present Argotario, a serious game that deals with
fallacies in everyday argumentation. Argotario is a multilingual, open-source,
platform-independent application with strong educational aspects, accessible at
www.argotario.net.Comment: EMNLP 2017 demo paper. Source codes:
https://github.com/UKPLab/argotari
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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Innovating Pedagogy 2015: Open University Innovation Report 4
This series of reports explores new forms of teaching, learning and assessment for an interactive world, to guide teachers and policy makers in productive innovation. This fourth report proposes ten innovations that are already in currency but have not yet had a profound influence on education. To produce it, a group of academics at the Institute of Educational Technology in The Open University collaborated with researchers from the Center for Technology in Learning at SRI International. We proposed a long list of new educational terms, theories, and practices. We then pared these down to ten that have the potential to provoke major shifts in educational practice, particularly in post-school education. Lastly, we drew on published and unpublished writings to compile the ten sketches of new pedagogies that might transform education. These are summarised below in an approximate order of immediacy and timescale to widespread implementation
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