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Datenbank Spektrum 20(2) - Juli 2020

https://dl.gi.de/handle/20.500.12116/36362
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Auflistung Datenbank Spektrum 20(2) - Juli 2020 nach Schlagwort "Argument Mining"

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  • 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
    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.