27,979 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
Crowdsourcing Argumentation Structures in Chinese Hotel Reviews
Argumentation mining aims at automatically extracting the premises-claim
discourse structures in natural language texts. There is a great demand for
argumentation corpora for customer reviews. However, due to the controversial
nature of the argumentation annotation task, there exist very few large-scale
argumentation corpora for customer reviews. In this work, we novelly use the
crowdsourcing technique to collect argumentation annotations in Chinese hotel
reviews. As the first Chinese argumentation dataset, our corpus includes 4814
argument component annotations and 411 argument relation annotations, and its
annotations qualities are comparable to some widely used argumentation corpora
in other languages.Comment: 6 pages,3 figures,This article has been submitted to "The 2017 IEEE
International Conference on Systems, Man, and Cybernetics (SMC2017)
Joint RNN Model for Argument Component Boundary Detection
Argument Component Boundary Detection (ACBD) is an important sub-task in
argumentation mining; it aims at identifying the word sequences that constitute
argument components, and is usually considered as the first sub-task in the
argumentation mining pipeline. Existing ACBD methods heavily depend on
task-specific knowledge, and require considerable human efforts on
feature-engineering. To tackle these problems, in this work, we formulate ACBD
as a sequence labeling problem and propose a variety of Recurrent Neural
Network (RNN) based methods, which do not use domain specific or handcrafted
features beyond the relative position of the sentence in the document. In
particular, we propose a novel joint RNN model that can predict whether
sentences are argumentative or not, and use the predicted results to more
precisely detect the argument component boundaries. We evaluate our techniques
on two corpora from two different genres; results suggest that our joint RNN
model obtain the state-of-the-art performance on both datasets.Comment: 6 pages, 3 figures, submitted to IEEE SMC 201
Neural End-to-End Learning for Computational Argumentation Mining
We investigate neural techniques for end-to-end computational argumentation
mining (AM). We frame AM both as a token-based dependency parsing and as a
token-based sequence tagging problem, including a multi-task learning setup.
Contrary to models that operate on the argument component level, we find that
framing AM as dependency parsing leads to subpar performance results. In
contrast, less complex (local) tagging models based on BiLSTMs perform robustly
across classification scenarios, being able to catch long-range dependencies
inherent to the AM problem. Moreover, we find that jointly learning 'natural'
subtasks, in a multi-task learning setup, improves performance.Comment: To be published at ACL 201
A pilot study in using argumentation frameworks for online debates
We describe a pilot study in using argumentation frameworks obtained from an online debate to evaluate positions expressed in the debate. This pilot study aims at exploring the richness of Computational Argumentation methods and techniques for evaluating arguments to reason with the output of Argument Mining. It uses a hand-generated graphical representation of the debate as an intermediate representation from which argumentation frameworks can be extracted, but richer than any existing argumentation framework. The intermediate representation can provide insights for benchmark sets derived from online debates
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