Auflistung Datenbank Spektrum 20(2) - Juli 2020 nach Erscheinungsdatum
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- ZeitschriftenartikelHow to Win Arguments(Datenbank-Spektrum: Vol. 20, No. 2, 2020) Weber, Klaus; Rach, Niklas; Minker, Wolfgang; André, ElisabethPeople make decisions every day or form an opinion based on persuasion processes, whether through advertising, planning leisure activities with friends or public speeches. Most of the time, however, subliminal persuasion processes triggered by behavioral cues (rather than the content of the message) play a far more important role than most people are aware of. To raise awareness of the different aspects of persuasion ( how and what ), we present a multimodal dialog system consisting of two virtual agents that use synthetic speech in a discussion setting to present pros and cons to a user on a controversial topic. The agents are able to adapt their emotions based on explicit feedback of the users to increase their perceived persuasiveness during interaction using Reinforcement Learning.
- ZeitschriftenartikelNews(Datenbank-Spektrum: Vol. 20, No. 2, 2020)
- ZeitschriftenartikelTowards Understanding and Arguing with Classifiers: Recent Progress(Datenbank-Spektrum: Vol. 20, No. 2, 2020) Shao, Xiaoting; Rienstra, Tjitze; Thimm, Matthias; Kersting, KristianMachine learning and argumentation can potentially greatly benefit from each other. Combining deep classifiers with knowledge expressed in the form of rules and constraints allows one to leverage different forms of abstractions within argumentation mining. Argumentation for machine learning can yield argumentation-based learning methods where the machine and the user argue about the learned model with the common goal of providing results of maximum utility to the user. Unfortunately, both directions are currently rather challenging. For instance, combining deep neural models with logic typically only yields deterministic results, while combining probabilistic models with logic often results in intractable inference. Therefore, we review a novel deep but tractable model for conditional probability distributions that can harness the expressive power of universal function approximators such as neural networks while still maintaining a wide range of tractable inference routines. While this new model has shown appealing performance in classification tasks, humans cannot easily understand the reasons for its decision. Therefore, we also review our recent efforts on how to “argue” with deep models. On synthetic and real data we illustrate how “arguing” with a deep model about its explanations can actually help to revise the model, if it is right for the wrong reasons.
- ZeitschriftenartikelAnalysis of Political Debates through Newspaper Reports: Methods and Outcomes(Datenbank-Spektrum: Vol. 20, No. 2, 2020) Lapesa, Gabriella; Blessing, Andre; Blokker, Nico; Dayanik, Erenay; Haunss, Sebastian; Kuhn, Jonas; Padó, SebastianDiscourse network analysis is an aspiring development in political science which analyzes political debates in terms of bipartite actor/claim networks. It aims at understanding the structure and temporal dynamics of major political debates as instances of politicized democratic decision making. We discuss how such networks can be constructed on the basis of large collections of unstructured text, namely newspaper reports. We sketch a hybrid methodology of manual analysis by domain experts complemented by machine learning and exemplify it on the case study of the German public debate on immigration in the year 2015. The first half of our article sketches the conceptual building blocks of discourse network analysis and demonstrates its application. The second half discusses the potential of the application of NLP methods to support the creation of discourse network datasets.
- ZeitschriftenartikelRelational and Fine-Grained Argument Mining(Datenbank-Spektrum: Vol. 20, No. 2, 2020) Trautmann, Dietrich; Fromm, Michael; Tresp, Volker; Seidl, Thomas; Schütze, HinrichIn 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.
- ZeitschriftenartikelReconstructing Arguments from Noisy Text(Datenbank-Spektrum: Vol. 20, No. 2, 2020) Dykes, Natalie; Evert, Stefan; Göttlinger, Merlin; Heinrich, Philipp; Schröder, LutzSocial media are of paramount importance to public discourse. RANT aims to contribute methods and formalisms for extracting, representing, and processing arguments from noisy text found in social media discussions, using a large corpus of pre-referendum Brexit tweets as a running case study. We identify recurring linguistic argumentation patterns in a corpus-linguistic analysis and formulate corresponding corpus queries to extract arguments automatically. Given the huge amount of social media data available, our approach aims at high precision at the possible price of low recall. Argumentation patterns are directly associated with logical patterns in a dedicated formalism and accordingly, individual arguments are directly parsed as logical formulae. The logical formalism for argument representation features a broad range of modalities capturing real-life modes of expression. We cast this formalism as a family of instance logics in the generic framework of coalgebraic logic and complement it by a flexible framework to represent relationships between arguments; including standard relations like attack and support but also relations extracted from metadata. Some relations are inferred from the logical content of individual arguments. We are in the process of developing suitable generalizations of various extension semantics for argumentation frameworks combined with corresponding algorithmic methods to allow for the automated retrieval of large-scale argumentative positions.
- ZeitschriftenartikelThe 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, GerhardTwo 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.
- ZeitschriftenartikelThe ReCAP Project(Datenbank-Spektrum: Vol. 20, No. 2, 2020) Bergmann, Ralph; Biertz, Manuel; Dumani, Lorik; Lenz, Mirko; Ludwig, Anna-Katharina; Neumann, Patrick J.; Ollinger, Stefan; Sahitaj, Premtim; Schenkel, Ralf; Witry, AlexArgumentation Machines search for arguments in natural language from information sources on the Web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a particular user query. The recap project is part of the Priority Program ratio and aims at novel contributions to and confluence of methods from information retrieval, knowledge representation, as well as case-based reasoning for the development of future argumentation machines. In this paper we summarise recent research results from the project. In particular, a new German corpus of 100 semantically annotated argument graphs from the domain of education politics has been created and is made available to the argumentation research community. Further, we discuss a comprehensive investigation in finding arguments and argument graphs. We introduce a probabilistic ranking framework for argument retrieval, i.e. for finding good premises for a designated claim. For finding argument graphs, we developed methods for case-based argument retrieval considering the graph structure of an argument together with textual and ontology-based similarity measures applied to claims, premises, and argument schemes.
- ZeitschriftenartikelArgumenText: 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, IrynaThe 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.
- ZeitschriftenartikelEditorial(Datenbank-Spektrum: Vol. 20, No. 2, 2020) Cimiano, Philipp; Heyer, Gerhard; Kohlhase, Michael; Stein, Benno; Ziegler, Jürgen; Härder, Theo