2020Datenbank-SpektrumOpen access

The Road Map to FAME: A Framework for Mining and Formal Evaluation of Arguments

Ringo Baumann, Gregor Wiedemann, Maximilian Heinrich, Ahmad Dawar Hakimi, Gerhard Heyer

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Abstract

Abstract 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.

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Abstract 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.

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Available abstract

Abstract 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.

Key concepts: Argumentation theory, Argument (complex analysis), Argument map, Computer science, Representation (politics), Argumentation framework, Data science, Natural (archaeology)

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