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Auflistung nach Schlagwort "Argument Mining"

1 - 5 von 5
  • Zeitschriftenartikel
    Argument Mining on Twitter: A survey
    (it - Information Technology: Vol. 63, No. 1, 2021) Schaefer, Robin; Stede, Manfred
    In the last decade, the field of argument mining has grown notably. However, only relatively few studies have investigated argumentation in social media and specifically on Twitter. Here, we provide the, to our knowledge, first critical in-depth survey of the state of the art in tweet-based argument mining. We discuss approaches to modelling the structure of arguments in the context of tweet corpus annotation, and we review current progress in the task of detecting argument components and their relations in tweets. We also survey the intersection of argument mining and stance detection, before we conclude with an outlook.
  • Zeitschriftenartikel
    ArgumenText: Argument Classification and Clustering in a Generalized Search Scenario
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Daxenberger, Johannes; Schiller, Benjamin; Stahlhut, Chris; Kaiser, Erik; Gurevych, Iryna
    The ArgumenText project creates argument mining technology for big and heterogeneous data and aims to evaluate its use in real-world applications. The technology mines and clusters arguments from a variety of textual sources for a large range of topics and in multiple languages. Its main strength is its generalization to very different textual sources including web crawls, news data, or customer reviews. We validated the technology with a focus on supporting decisions in innovation management as well as customer feedback analysis. Along with its public argument search engine and API, ArgumenText has released multiple datasets for argument classification and clustering. This contribution outlines the major technology-related challenges and proposed solutions for the tasks of argument extraction from heterogeneous sources and argument clustering. It also lays out exemplary industry applications and remaining challenges.
  • Zeitschriftenartikel
    Relational and Fine-Grained Argument Mining
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Trautmann, Dietrich; Fromm, Michael; Tresp, Volker; Seidl, Thomas; Schütze, Hinrich
    In our project ReMLAV , funded within the DFG Priority Program RATIO ( http://www.spp-ratio.de/ ), we focus on relational and fine-grained argument mining. In this article, we first introduce the problems we address and then summarize related work. The main part of the article describes our research on argument mining, both coarse-grained and fine-grained methods, and on same-side stance classification, a relational approach to the problem of stance classification. We conclude with an outlook.
  • Zeitschriftenartikel
    Text Mining für Online-Partizipationsverfahren: Die Notwendigkeit einer maschinell unterstützten Auswertung
    (HMD Praxis der Wirtschaftsinformatik: Vol. 54, No. 4, 2017) Liebeck, Matthias; Esau, Katharina; Conrad, Stefan
    Online-Partizipationsverfahren werden in den letzten Jahren vermehrt von Städten und Gemeinden eingesetzt, um ihre Bürger in politische Entscheidungsprozesse einzubeziehen. Der vorliegende Beitrag beginnt mit einer Kategorisierung von Online-Partizipationsverfahren im politischen Kontext in Deutschland und fokussiert auf das Beteiligungsprojekt Tempelhofer Feld in Berlin. Dazu werden die Probleme einer manuellen Auswertung und die Notwendigkeit einer maschinell unterstützten Auswertung von Textbeiträgen aus Partizipationsverfahren beschrieben.Im Beitrag wird auf die Probleme und Lösungsmöglichkeiten in den drei Analysebereichen Argument Mining, Themenextraktion und Erkennung von Emotionen eingegangen. Für den Bereich Argument Mining wird ein geeignetes dreiteiliges Argumentationsmodell, welches auf das Online-Partizipationsverfahren Tempelhofer Feld der Stadt Berlin angewendet wird, diskutiert. Zudem wird der Einsatz von word embeddings als Features für eine Support Vector Machine zur automatisierten Klassifikation von Argumentationskomponenten evaluiert. Anschließend wird ein Einblick in das Aufgabengebiet der Themenextraktion, dessen Ziel die Erstellung eines groben Überblicks über die diskutierten Themen eines Online-Partizipationsverfahrens ist, gegeben und die Ergebnisse zweier Verfahren werden diskutiert. Danach erfolgt eine Diskussion über die Einsatzmöglichkeiten einer automatisierten Emotionserkennung im Kontext von Online-Partizipationsverfahren.AbstractIn recent years cities and municipalities rely increasingly on online participation processes to involve their citizens in political decision-making processes. This paper opens by categorizing political online participation processes in Germany before focusing on one participation project in particular, namely the Tempelhofer Feld in Berlin. In addition, the problems of a manual analysis of text contributions from participation processes are outlined in order to highlight the necessity for automatically supported evaluations.We discuss problems and possible solutions in three areas of analysis: argument mining, topic extraction, and emotion mining. For argument mining, a suitable three-part argumentation model is discussed which is applied to the online participation process Tempelhofer Feld. Furthermore, word embeddings are being evaluated as features for a support vector machine tasked with the automated classification of argumentation components. Subsequently, we focus on topic extraction, which aims to provide a rough overview of the topics discussed in an online participation process, and present the results of two methods. The paper concludes with a discussion on possible applications of an automated recognition of emotions in the context of online participation processes.
  • Zeitschriftenartikel
    The Road Map to FAME: A Framework for Mining and Formal Evaluation of Arguments
    (Datenbank-Spektrum: Vol. 20, No. 2, 2020) Baumann, Ringo; Wiedemann, Gregor; Heinrich, Maximilian; Hakimi, Ahmad Dawar; Heyer, Gerhard
    Two different perspectives on argumentation have been pursued in computer science research, namely approaches of argument mining in natural language processing on the one hand, and formal argument evaluation on the other hand. So far these research areas are largely independent and unrelated. This article introduces the agenda of our recently started project “FAME – A framework for argument mining and evaluation”. The main project idea is to link the two perspectives on argumentation and their respective research agendas by employing controlled natural language as a convenient form of intermediate knowledge representation. Our goal is to develop a framework which integrates argument mining and formal argument evaluation to study patterns of empirical argumentation usage. If successful, this combination will allow for new types of queries to be answered by argumentation retrieval systems and large-scale content analysis. Moreover, feeding evaluation results as additional knowledge input to argument mining processes could be utilized to further improve their results.