19,109 research outputs found
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
A Labelling Framework for Probabilistic Argumentation
The combination of argumentation and probability paves the way to new
accounts of qualitative and quantitative uncertainty, thereby offering new
theoretical and applicative opportunities. Due to a variety of interests,
probabilistic argumentation is approached in the literature with different
frameworks, pertaining to structured and abstract argumentation, and with
respect to diverse types of uncertainty, in particular the uncertainty on the
credibility of the premises, the uncertainty about which arguments to consider,
and the uncertainty on the acceptance status of arguments or statements.
Towards a general framework for probabilistic argumentation, we investigate a
labelling-oriented framework encompassing a basic setting for rule-based
argumentation and its (semi-) abstract account, along with diverse types of
uncertainty. Our framework provides a systematic treatment of various kinds of
uncertainty and of their relationships and allows us to back or question
assertions from the literature
The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants
Reasoning is a crucial part of natural language argumentation. To comprehend
an argument, one must analyze its warrant, which explains why its claim follows
from its premises. As arguments are highly contextualized, warrants are usually
presupposed and left implicit. Thus, the comprehension does not only require
language understanding and logic skills, but also depends on common sense. In
this paper we develop a methodology for reconstructing warrants systematically.
We operationalize it in a scalable crowdsourcing process, resulting in a freely
licensed dataset with warrants for 2k authentic arguments from news comments.
On this basis, we present a new challenging task, the argument reasoning
comprehension task. Given an argument with a claim and a premise, the goal is
to choose the correct implicit warrant from two options. Both warrants are
plausible and lexically close, but lead to contradicting claims. A solution to
this task will define a substantial step towards automatic warrant
reconstruction. However, experiments with several neural attention and language
models reveal that current approaches do not suffice.Comment: Accepted as NAACL 2018 Long Paper; see details on the front pag
A QBF-based Formalization of Abstract Argumentation Semantics
Supported by the National Research Fund, Luxembourg (LAAMI project) and by the Engineering and Physical Sciences Research Council (EPSRC, UK), grant ref. EP/J012084/1 (SAsSY project).Peer reviewedPostprin
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