The evolution of argumentation mining: From models to social media and emerging tools |
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| Institution: | 1. Department of Computer Science, The University of Sheffield, Sheffield, UK;2. Computer Science Department, The University of Sheffield International Faculty - CITY College, Thessaloniki, Greece;3. South East European Research Centre (SEERC), Thessaloniki, Greece;4. Department of Informatics and Telecommunications Engineering, University of Western Macedonia, Kozani, Greece;1. School of Data and Computer Science, Sun Yat-sen University, China;2. School of Computing Science, University of Glasgow, Glasgow, UK;3. School of Computer Science, The University of Adelaide, Adelaide, Australia;1. Department of Information Management, Peking University, No. 5 Yiheyuan Road Haidian District, Beijing, 100871, P.R. China;2. China Center for Special Economic Zone Research, Shenzhen University, Nanhai Ave 3688 Shenzhen, Shenzhen, Guandong 518060, China;1. University of North Carolina at Chapel Hill, United States;2. Data Science, Facebook 1 Hacker Way Menlo Park, CA 94025, United States;3. User Experience and Customer Insights, NetBrain 15 Network Drive Burlington, MA, 01803, United States |
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| Abstract: | Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks. |
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