32 research outputs found
Langage, post-politique et automatisation : critique préventive de l’argumentation artificielle
Cet article met en discussion les concepts de post-politique, de gouvernementalité algorithmique et de langage en traçant un portrait des recherches récentes dans le champ de l’argumentation mining, un champ de recherche en Big Data et en linguistique computationnelle. Il montre que les recherches en argumentation mining portent un intérêt particulier au débat politique, et offrent des méthodes qui participent au processus de dépolitisation du débat, mais sont encore loin de pouvoir saisir toutes les spécificités du langage.This article discusses the concepts of post-political, algorithmic governance and language in relation to recent research in argumentation mining, a field of study within Big Data and computational linguistics. This article shows that studies in argumentation mining are particularly oriented towards political debate and offer methods to depoliticize the debate process, but are still incapable of grasping all of language’s specificities.Este artÃculo analiza los conceptos de post-polÃtica, gubernamentalidad algorÃtmica y de lenguaje, estableciendo un panorama de las investigaciones recientes en el campo de la minerÃa de argumentación, un campo de investigación que relaciona Big Data y lingüÃstica computacional. Se concluye que las investigaciones en minerÃa de argumentos tienen un interés particular en el debate polÃtico a la vez que ofrecen métodos que contribuyen a la despolitización del debate; y que aún están lejos de poder capturar todas las especificidades del lenguaje
Unleashing the Potential of Argument Mining for IS Research: A Systematic Review and Research Agenda
Argument mining (AM) represents the unique use of natural language processing (NLP) techniques to extract arguments from unstructured data automatically. Despite expanding on commonly used NLP techniques, such as sentiment analysis, AM has hardly been applied in information systems (IS) research yet. Consequentially, knowledge about the potentials for the usage of AM on IS use cases appears to be still limited. First, we introduce AM and its current usage in fields beyond IS. To address this research gap, we conducted a systematic literature review on IS literature to identify IS use cases that can potentially be extended with AM. We develop eleven text-based IS research topics that provide structure and context to the use cases and their AM potentials. Finally, we formulate a novel research agenda to guide both researchers and practitioners to design, compare and evaluate the use of AM for text-based applications and research streams in IS
Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning
Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal
Argumentative Link Prediction using Residual Networks and Multi-Objective Learning.
We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. We propose a domain-agnostic method that makes no assumptions on document or argument structure. We evaluate our method on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge
Empowering NGOs in Countering Online Hate Messages
Studies on online hate speech have mostly focused on the automated detection
of harmful messages. Little attention has been devoted so far to the
development of effective strategies to fight hate speech, in particular through
the creation of counter-messages. While existing manual scrutiny and
intervention strategies are time-consuming and not scalable, advances in
natural language processing have the potential to provide a systematic approach
to hatred management. In this paper, we introduce a novel ICT platform that NGO
operators can use to monitor and analyze social media data, along with a
counter-narrative suggestion tool. Our platform aims at increasing the
efficiency and effectiveness of operators' activities against islamophobia. We
test the platform with more than one hundred NGO operators in three countries
through qualitative and quantitative evaluation. Results show that NGOs favor
the platform solution with the suggestion tool, and that the time required to
produce counter-narratives significantly decreases.Comment: Preprint of the paper published in Online Social Networks and Media
Journal (OSNEM
Multi-Task Attentive Residual Networks for Argument Mining
We explore the use of residual networks and neural attention for argument
mining and in particular link prediction. The method we propose makes no
assumptions on document or argument structure. We propose a residual
architecture that exploits attention, multi-task learning, and makes use of
ensemble. We evaluate it on a challenging data set consisting of user-generated
comments, as well as on two other datasets consisting of scientific
publications. On the user-generated content dataset, our model outperforms
state-of-the-art methods that rely on domain knowledge. On the scientific
literature datasets it achieves results comparable to those yielded by
BERT-based approaches but with a much smaller model size.Comment: 12 pages, 2 figures, submitted to IEEE Transactions on Neural
Networks and Learning System
Software Support for Discourse-Based Textual Information Analysis: A Systematic Literature Review and Software Guidelines in Practice
[Abstract]
The intrinsic characteristics of humanities research require technological support and software assistance that also necessarily goes through the analysis of textual narratives. When these narratives become increasingly complex, pragmatics analysis (i.e., at discourse or argumentation levels) assisted by software is a great ally in the digital humanities. In recent years, solutions have been developed from the information visualization domain to support discourse analysis or argumentation analysis of textual sources via software, with applications in political speeches, debates, online forums, but also in written narratives, literature or historical sources. This paper presents a wide and interdisciplinary systematic literature review (SLR), both in software-related areas and humanities areas, on the information visualization and the software solutions adopted to support pragmatics textual analysis. As a result of this review, this paper detects weaknesses in existing works on the field, especially related to solutions’ availability, pragmatic framework dependence and lack of information sharing and reuse software mechanisms. The paper also provides some software guidelines for improving the detected weaknesses, exemplifying some guidelines in practice through their implementation in a new web tool, Viscourse. Viscourse is conceived as a complementary tool to assist textual analysis and to facilitate the reuse of informational pieces from discourse and argumentation text analysis tasks.Ministerio de EconomÃa, Industria y Competitividad; FJCI-2016-6 28032Ministerio de Ciencia, Innovación y Universidades; RTI2018-093336-B-C2