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Argument Extraction from News, Blogs, and Social Media

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Artificial Intelligence: Methods and Applications (SETN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8445))

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Abstract

Argument extraction is the task of identifying arguments, along with their components in text. Arguments can be usually decomposed into a claim and one or more premises justifying it. 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. The huge increase of social media communities, along with their user tendency to debate, makes the identification of arguments in these texts a necessity. 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. In addition, being less formal in nature, texts in Social Media may not even have proper syntax or spelling. This paper presents a two-step approach for argument extraction from social media texts. During the first step, the proposed approach tries to classify the sentences into “sentences that contain arguments” and “sentences that don’t contain arguments”. 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. The results exceed significantly the base line approach, and according to literature, are quite promising.

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Goudas, T., Louizos, C., Petasis, G., Karkaletsis, V. (2014). Argument Extraction from News, Blogs, and Social Media. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_23

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  • DOI: https://doi.org/10.1007/978-3-319-07064-3_23

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07063-6

  • Online ISBN: 978-3-319-07064-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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