Dykes N, Evert S, Heinrich P, Humml M, Schröder L (2024)
Publication Type: Conference contribution
Publication year: 2024
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 14638 LNAI
Pages Range: 163-181
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Bielefeld, DEU
ISBN: 9783031635359
DOI: 10.1007/978-3-031-63536-6_10
We are concerned with extracting argumentative fragments from social media, exemplified with a case study on a large corpus of English tweets about the UK Brexit referendum in 2016. Our overall approach is to parse the corpus using dedicated corpus queries that fill designated slots in predefined logical patterns. We present an inventory of logical patterns and corresponding queries, which have been carefully designed and refined. While a gold standard of substantial size is difficult to obtain by manual annotation, our queries can retrieve hundreds of thousands of examples with high precision. We show how queries can be combined to extract complex nested statements relevant to argumentation. We also show how to proceed for applications needing higher recall: high-precision query matches can be used as training data for an LLM classifier, and the trade-off between precision and recall can be freely adjusted with its cutoff threshold.
APA:
Dykes, N., Evert, S., Heinrich, P., Humml, M., & Schröder, L. (2024). Finding Argument Fragments on Social Media with Corpus Queries and LLMs. In Philipp Cimiano, Anette Frank, Michael Kohlhase, Benno Stein (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 163-181). Bielefeld, DEU: Springer Science and Business Media Deutschland GmbH.
MLA:
Dykes, Nathan, et al. "Finding Argument Fragments on Social Media with Corpus Queries and LLMs." Proceedings of the 1st International Conference on Robust Argumentation Machines, RATIO 2024, Bielefeld, DEU Ed. Philipp Cimiano, Anette Frank, Michael Kohlhase, Benno Stein, Springer Science and Business Media Deutschland GmbH, 2024. 163-181.
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