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 Pilot Study on Argument Simplification in Stance-Based Opinions

Prior work has investigated the problem mining arguments from online reviews by classifying opinions based on the stance expressed explicitly or implicitly. An implicit opinion has the stance left unexpressed linguistically while an explicit opinion has the stance expressed explicitly. In this paper, we propose a bipartite graph-based approach to relate a given set of explicit opinions as simplified arguments for a given set of implicit opinions using three different features (a) sentence similarity, (b) sentiment and (c) target. Experiments are carried out on a manually annotated set of explicit-implicit opinions and show that unsupervised sentence representations can be used to accurately match arguments with their corresponding simplified versions

unsupervised detection of argumentative units though topic modeling techniques

n/

Never Retreat, Never Retract: Argumentation Analysis for Political Speeches

International audienceIn this work, we apply argumentation mining techniques, in particular relation prediction, to study political speeches in monological form, where there is no direct interaction between opponents. We argue that this kind of technique can effectively support researchers in history, social and political sciences, which must deal with an increasing amount of data in digital form and need ways to automatically extract and analyse argumentation patterns. We test and discuss our approach based on the analysis of documents issued by R. Nixon and J. F. Kennedy during 1960 presidential campaign. We rely on a supervised classifier to predict argument relations (i.e., support and attack), obtaining an accuracy of 0.72 on a dataset of 1,462 argument pairs. The application of argument mining to such data allows not only to highlight the main points of agreement and disagreement between the candidates' arguments over the campaign issues such as Cuba, disarmament and health-care, but also an in-depth argumentative analysis of the respective viewpoints on these topics

Argumentation models and their use in corpus annotation: practice, prospects, and challenges

The study of argumentation is transversal to several research domains, from philosophy to linguistics, from the law to computer science and artificial intelligence. In discourse analysis, several distinct models have been proposed to harness argumentation, each with a different focus or aim. To analyze the use of argumentation in natural language, several corpora annotation efforts have been carried out, with a more or less explicit grounding on one of such theoretical argumentation models. In fact, given the recent growing interest in argument mining applications, argument-annotated corpora are crucial to train machine learning models in a supervised way. However, the proliferation of such corpora has led to a wide disparity in the granularity of the argument annotations employed. In this paper, we review the most relevant theoretical argumentation models, after which we survey argument annotation projects closely following those theoretical models. We also highlight the main simplifications that are often introduced in practice. Furthermore, we glimpse other annotation efforts that are not so theoretically grounded but instead follow a shallower approach. It turns out that most argument annotation projects make their own assumptions and simplifications, both in terms of the textual genre they focus on and in terms of adapting the adopted theoretical argumentation model for their own agenda. Issues of compatibility among argument-annotated corpora are discussed by looking at the problem from a syntactical, semantic, and practical perspective. Finally, we discuss current and prospective applications of models that take advantage of argument-annotated corpora

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

ArgMine: Argumentation Mining from Text

O objetivo da prospeção de argumentos a partir de texto é a deteção e identificação de forma automática da estrutura argumentativa contida num texto escrito em linguagem natural.Um argumento é uma estrutura retórica que é estudada desde à muitos anos e que se encontra bem fundamentada. De uma forma geral, argumentos são posições justificáveis onde factos (premissas) são apresentados em suporte de uma conclusão.A ambiguidade do texto escrito em linguagem natural, diferentes estilos de escrita, contexto implícito e a complexidade em construir estruturas argumentativas são alguns dos desafios que fazem desta tarefa muito desafiadora.Extraindo de forma automática argumentos a partir de texto, somos capazes de saber não apenas quais são os pontos de vista que estão a ser expressos, mas também quais são as razões para acreditar nesse pontos de vista. Assim sendo, a prospeção de argumentos de forma automática tem o potencial de trazer avanços em algumas áreas de investigação tais como prospeção de opiniões, sistemas de recomendação e sistemas multi-agente.A tarefa completa de prospeção de argumentos pode ser decomposta em várias sub-tarefas. Esta tese aborda a deteção e identificação, de forma automática, dos componentes argumentativos presentes no texto. Isso envolve detetar as zonas do texto que contêm conteúdo argumentativo e, a seleção dos fragmentos de texto que correspondem às unidades elementares de argumentos. Para que seja possível de uma forma automática detetar e identificar componentes argumentativos a partir de texto, algoritmos de aprendizagem máquina supervisionada serão usados.O conjunto de dados alvo que será usado para treinar os algoritmos são noticias escritas na língua Portuguesa.The aim of argumentation mining is the automatic detection and identification of the argumentative structure contained within a piece of natural language text. An argument is an ancient and well studied rhetorical structure. In a general form, arguments are justifiable positions where pieces of evidence (premises) are offered in support of a conclusion. The ambiguity of natural language text, different writing styles, implicit context and the complexity of building argument structures are some of the challenges which make this task very challenging. By automatically extracting arguments from text, we are able to tell not just what views are being expressed, but also what are the reasons to believe those particular views. Therefore, argumentation mining has the potential to improve some research topics such as opinion mining, recommender systems and multi-agent systems. The full task of argumentation mining can be decomposed into several subtasks. This thesis focuses on the automatic detection and identification of the argumentative components presented in the original text. This involves detecting the zones of text that contain argumentative content and the identification of fragments of text that will form the elementary units of the argument. In order to automatically detect and identify argumentative components in text, supervised machine learning algorithms will be used. The target corpus used to train the algorithms are news written in Portuguese language

Never Retreat, Never Retract: Argumentation Analysis for Political Speeches

In this work, we apply argumentation mining techniques, in particular relation prediction, to study political speeches in monological form, where there is no direct interaction between opponents. We argue that this kind of technique can effectively support researchers in history, social and political sciences, which must deal with an increasing amount of data in digital form and need ways to automatically extract and analyse argumentation patterns. We test and discuss our approach based on the analysis of documents issued by R. Nixon and J. F. Kennedy during 1960 presidential campaign. We rely on a supervised classifier to predict argument relations (i.e., support and attack), obtaining an accuracy of 0.72 on a dataset of 1,462 argument pairs. The application of argument mining to such data allows not only to highlight the main points of agreement and disagreement between the candidates' arguments over the campaign issues such as Cuba, disarmament and health-care, but also an in-depth argumentative analysis of the respective viewpoints on these topics