Reconstructing Arguments from Noisy Text

Dykes N, Evert S, Göttlinger M, Heinrich P, Schröder L (2020)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2020


Book Volume: 20

Pages Range: 123-129

Issue: 2


DOI: 10.1007/s13222-020-00342-y

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Social 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.

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Dykes, N., Evert, S., Göttlinger, M., Heinrich, P., & Schröder, L. (2020). Reconstructing Arguments from Noisy Text. Datenbank-Spektrum, 20, 123-129.


Dykes, Nathan, et al. "Reconstructing Arguments from Noisy Text." Datenbank-Spektrum 20 (2020): 123-129.

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