4,901 research outputs found
An Informatics Perspective on Argumentation Mining
Abstract It is time to develop a community research agenda in argumentation mining. I suggest some questions to drive a joint community research agenda and then explain how my research in argumentation, on support tools and knowledge representations, advances argumentation mining
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A short survey of discourse representation models
With the advancement of technology and the wide adoption of ontologies as knowledge representation formats, in the last decade, a handful of models were proposed for the externalization of the rhetoric and argumentation captured within scientific publications. Conceptually, most of these models share a similar representation form of the scientific publication, i.e. as a series of interconnected elementary knowledge items. The main differences are given by the terminology used, the types of rhetorical and/or argumentation relations connecting the knowledge items and the foundational theories supporting these relations. This paper analyzes the state of the art and provides a concise comparative overview of the five most prominent discourse representation models, with the goal of sketching an unified model for discourse representation
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
Rationale in Development Chat Messages: An Exploratory Study
Chat messages of development teams play an increasingly significant role in
software development, having replaced emails in some cases. Chat messages
contain information about discussed issues, considered alternatives and
argumentation leading to the decisions made during software development. These
elements, defined as rationale, are invaluable during software evolution for
documenting and reusing development knowledge. Rationale is also essential for
coping with changes and for effective maintenance of the software system.
However, exploiting the rationale hidden in the chat messages is challenging
due to the high volume of unstructured messages covering a wide range of
topics. This work presents the results of an exploratory study examining the
frequency of rationale in chat messages, the completeness of the available
rationale and the potential of automatic techniques for rationale extraction.
For this purpose, we apply content analysis and machine learning techniques on
more than 8,700 chat messages from three software development projects. Our
results show that chat messages are a rich source of rationale and that machine
learning is a promising technique for detecting rationale and identifying
different rationale elements.Comment: 11 pages, 6 figures. The 14th International Conference on Mining
Software Repositories (MSR'17
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
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