32,188 research outputs found
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)
Summarizing Dialogic Arguments from Social Media
Online argumentative dialog is a rich source of information on popular
beliefs and opinions that could be useful to companies as well as governmental
or public policy agencies. Compact, easy to read, summaries of these dialogues
would thus be highly valuable. A priori, it is not even clear what form such a
summary should take. Previous work on summarization has primarily focused on
summarizing written texts, where the notion of an abstract of the text is well
defined. We collect gold standard training data consisting of five human
summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control
and Abortion. We present several different computational models aimed at
identifying segments of the dialogues whose content should be used for the
summary, using linguistic features and Word2vec features with both SVMs and
Bidirectional LSTMs. We show that we can identify the most important arguments
by using the dialog context with a best F-measure of 0.74 for gun control, 0.71
for gay marriage, and 0.67 for abortion.Comment: Proceedings of the 21th Workshop on the Semantics and Pragmatics of
Dialogue (SemDial 2017
Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion
Americans spend about a third of their time online, with many participating
in online conversations on social and political issues. We hypothesize that
social media arguments on such issues may be more engaging and persuasive than
traditional media summaries, and that particular types of people may be more or
less convinced by particular styles of argument, e.g. emotional arguments may
resonate with some personalities while factual arguments resonate with others.
We report a set of experiments testing at large scale how audience variables
interact with argument style to affect the persuasiveness of an argument, an
under-researched topic within natural language processing. We show that belief
change is affected by personality factors, with conscientious, open and
agreeable people being more convinced by emotional arguments.Comment: European Chapter of the Association for Computational Linguistics
(EACL 2017
Argument Compound Mining in Technical Texts: linguistic structures, implementation and annotation schemas
International audienceIn this paper, we motivate and develop the linguistic characteristics of argument compounds. The discourse structures that refine or elaborate arguments are analysed and their cognitive impact in argumentation is developed. An implementation is then presented. It is carried out in Dislog on the TextCoop platform. Dislog allows high level specifications in logic for fast and easy prototyping at a high level of linguistic adequacy. Elements of an indicative evaluation are provided
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