![]() ![]() MIT Press (2008)īlair, J., Anthony Tindale, C.W.: Groundwork in the Theory of Argumentation. This process is experimental and the keywords may be updated as the learning algorithm improves.īesnard, P., Hunter, A.: Elements of Argumentation. These keywords were added by machine and not by the authors. The results exceed significantly the base line approach, and according to literature, are quite promising. In the second step, it tries to identify the exact fragments that contain the premises from the sentences that contain arguments, by utilizing conditional random fields. ![]() ![]() During the first step, the proposed approach tries to classify the sentences into “sentences that contain arguments” and “sentences that don’t contain arguments”. This paper presents a two-step approach for argument extraction from social media texts. In addition, being less formal in nature, texts in Social Media may not even have proper syntax or spelling. Argument extraction from Social Media is more challenging because texts may not always contain arguments, as is the case of legal documents or scientific publications usually studied. The huge increase of social media communities, along with their user tendency to debate, makes the identification of arguments in these texts a necessity. Among the novel aspects of this work is the thematic domain itself which relates to Social Media, in contrast to traditional research in the area, which concentrates mainly on law documents and scientific publications. Arguments can be usually decomposed into a claim and one or more premises justifying it. Argument extraction is the task of identifying arguments, along with their components in text. ![]()
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