Corpus linguistics for history : the methodology of investigating place-name discourses in digitised nineteenth-century newspapers
The increasing availability of historical sources in a digital form has led to calls for new forms of reading in history. This thesis responds to these calls by exploring the potential of approaches from the field of corpus linguistics to be useful to historical research. Specifically, two sets of methodological issues are considered that arise when corpus linguistic methods are used on digitised historical sources. The first set of issues surrounds optical character recognition (OCR), computerised text transcription based on image reproduction of the original printed source. This process is error-prone, which leads to potentially unreliable word-counts. I find that OCR errors are very varied, and more different from their corrections than natural spelling variation from a standard form. As a result of OCR errors, the test OCR corpus examined has a slightly inflated overall token count (as compared to a hand-corrected gold standard), and a vastly inflated type count. Not all spurious types are infrequent: around 7% of types occurring at least 10 times in my test OCR corpus are spurious. I also find evidence that real-word errors occur. Assessing the impact of OCR errors on two common collocation statistics,
Joulain, Amelia Tahirih
Corpus linguistics for history : the methodology of investigating place-name discourses in digitised nineteenth-century newspapers. The increasing availability of historical sources in a digital form has led to calls for new forms of reading in history. This thesis responds to these calls by exploring the potential of approaches from the field of corpus linguistics to be useful to historical research. Specifically, two sets of methodological issues are considered that arise when corpus linguistic methods are used on digitised historical sources. The first set of issues surrounds optical character recognition (OCR), computerised text transcription based on image reproduction of the original printed source. This process is error-prone, which leads to potentially unreliable word-counts. I find that OCR errors are very varied, and more different from their corrections than natural spelling variation from a standard form. As a result of OCR errors, the test OCR corpus examined has a slightly inflated overall token count (as compared to a hand-corrected gold standard), and a vastly inflated type count. Not all spurious types are infrequent: around 7% of types occurring at least 10 times in my test OCR corpus are spurious. I also find evidence that real-word errors occur. Assessing the impact of OCR errors on two common collocation statistics,
corpus_linguistics history investigating place_name discourse digitise nineteenth_century newspaper increase availability historical_source digital form lead call new_form read history responds call explore_potential approach field corpus_linguistics useful historical research specifically two_set methodological_issue consider arise corpus_linguistic method_use historical_source first_set issue_surround optical character recognition computerise text transcription base image reproduction original print_source process error prone lead potentially unreliable word error vary different correction natural spell variation standard form error test corpus examine slightly overall token count compare hand gold standard vastly type count type infrequent around type times test corpus also evidence real word error occur assessing impact error common collocation statistic