6.2. Focus: testing the first version of the analytical tool

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Around the turn of the millennium, there has been a large increase in building up corpora for various purposes, and at the same time, several tools have been developed for their annotation and exploration. These tools are usually designed for tracking lexical and grammatical features (e.g., WordNet: Teich & Fankhauser, 2005; Coh-Metrix: Dufty et al., 2004, Green, 2012; TOPIC system: Benbrahim & Ahmad, 1994). Textual features that require background knowledge for interpretation, such as referential cohesion, are presently not possible to analyze automatically, as the antecedents of referring items can be of any length anywhere in the preceding text. Besides, the representation of cohesive reference is the reflection of one person’s – the analyst’s – interpretation of a text. While manual discourse analysis is extremely time-consuming, it can be supported by automating some parts of such an analysis.

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This first study intends to contribute to the analysis of referential cohesion by revising part of Halliday and Hasan’s (1976) slightly outdated but still frequently cited and used theory-based taxonomy of cohesion. This chapter describes the process of developing and piloting a new analytical tool for Referential Cohesion Analysis. Modifications were made with the intention of improving its applicability for the analysis of larger corpora of texts in a more reliable way. The process is supported by an exploratory analysis of a corpus of 10 research articles (RAs) to reveal what information about cohesive referential patterns in research articles Referential Cohesion Analysis yields – especially as compared to Halliday and Hasan’s (1976) approach – and to summarize the emergent hypotheses and further research questions that have been raised throughout the analytical process. Results from the analysis of the 10 RAs show that the analytical tool can capture patterns in referential cohesion that have not been accounted for by Halliday and Hasan’s (1976) taxonomy, for example, chains of referential cohesive ties. As a result of the clear visual representation of cohesive chains, referential links become easily searchable; furthermore, some calculations can be automated even in a large corpus, which hopefully enhances the reliability of the analysis.
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