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SUMMARY:Nathan Dykes: Studying argumentation patterns through corpus q
 ueries
UID:b24a-26b6-7d9e-67529@www.dhss.phil.fau.de
DESCRIPTION:The Department of Digital Humanities and Social Studies wo
 uld like to invite you to the following talk in our DH Colloquium: Nat
 han Dykes: »Studying argumentation patterns through corpus queries« 
 Abstract Corpus-Based Discourse Analysis typically relies on keywords 
 or collocations to identify patterns in large bodies of text. While th
 ese approaches work well to identify broad themes and attitudes in cor
 pora\, applying them to the study of complex propositions is challengi
 ng. In this presentation\, corpus queries are proposed as an entry poi
 nt to exploring statements relevant to argumentation. Using a collecti
 on of German media articles on multidrug-resistant organisms (MDRO)\, 
 a range of corpus queries were developed with the CQP query language (
 Evert & Hardie 2011). The queries combine regular expressions and word
 -lists and are built to reflect different linguistic realizations of a
 rgumentative patterns (cf. Dykes et al. 2022). Queries were developed 
 for two types of statements: causality and reported speech. The causal
  queries retrieve realizations of predetermined argumentative patterns
 \, such as those used by Dykes & Peters (2020) in their keyword study 
 on the same corpus. For example\, the negligence topos highlights a la
 ck of hygiene measures in hospitals as a central cause for the spread 
 of MDRO. In this study\, candidate words filter the query results\, pr
 oviding a more targeted approach than keywords. The quotation patterns
  were investigated in two ways. Firstly\, a sample of matches was anno
 tated to compare gender patterns regarding speakers and reporting verb
 s\, similar to Taboada (2024). Secondly\, the quotations themselves we
 re clustered based on sentence similarity\, grouping these complex sta
 tements. The results suggest that corpus queries are a promising route
  for discourse analysts. For the retrieval of predetermined argument p
 atterns\, filtering causal queries achieves considerably higher precis
 ion than keywords alone. The exploration of quotes rev
DTSTART:20241210T181500Z
DTEND:20241210T194500Z
LOCATION:Werner-von-Siemens-Straße 61 (Raum 3.17)\, 91052 Erlangen
DTSTAMP:20260418T170617Z
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