English grammar books are the genre that mirrors the development of language norms and language use most clearly. By stating the defects of language use and by criticising the seemingly inadequate works of predecessors and contemporaries, many grammarians authorise themselves to publish their own, better grammars. The high number of references to other grammars and grammarians shows that grammar books are usually not produced out of thin air, but that they emerge on the basis of the works of others, whether they are cited as authorities or criticised.
Our presentation aims at visualising such a network of grammars and grammarians by performing a network analysis on an XML-annotated corpus of 19th-century English grammars. The ties between authors will be examined quantitatively, i.e. in terms of the number of references, and qualitatively, i.e. by classifying and comparing different kinds of references, e.g. quotation, approval, the citing of authorities, and forms of criticism.
In so doing, a network of grammars and grammarians will become visible, which mirrors complex relations between authors, clusters of authors, isolated grammarians, and – viewed diachronically – the development of a grammarian's standing in the linguistic community.
The corpus contains the 50 most well-known and widely distributed grammars of the 19th century (cf. Leitner 1986, 1991, Linn 2006, Michael 1987, Görlach 1998) as full texts in digitised form. References to other grammarians are annotated, including the referenced author and work, and the kind of reference, as attributes. A list of grammarians' names, i.e. predecessors and contemporaries, is used as search terms to produce an adjacency matrix on the basis of the corpus. For visualisation and analysis, this matrix is then transformed into a directed weighed graph.
This combination of corpus-based historical linguistics (see Jucker and Taavitsainen 2014: 4) and network analysis (see, for instance, Freeman 2004, White 2011) is rather uncharted territory. In recent years, network analyses have been conducted using social media data, literature, or drama (e.g. Elson et al. 2010), but so far there have not been any similar studies on historical non-fictional texts.
Apart from the presentation of results, we would like to discuss how well such a network approach can mirror connections between grammarians and by which other means results can be refined. Moreover, we are interested in discussing practical aspects of network analysis, such as levels of error tolerance and means of (semi-automated) error avoidance with respect to wrong hits in the data.
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