title: Corpus linguistics for History
title: the methodology of investigating place-name discourses in digitised
      nineteenth-century newspapers
titleType: Subtitle
Creators: name: Joulain, Amelia Tahirih
Publisher: name: Lancaster University
Year: 2017
Subjects: corpus linguistics
optical character recognition
spatial humanities
collocation
nineteenth-century newspapers

description: 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,
      Mutual Information (MI) and Log-Likelihood (LL), I find that both are
      affected by OCR errors. This analysis also provides evidence that OCR
      errors are not homogenously distributed throughout the corpus.
      Nevertheless, for small collocation spans, MI rankings are broadly
      reliable in OCR data, especially when used in combination with an LL
      threshold. Large spans are best avoided, as both statistics become
      increasingly less reliable in OCR data, when used with larger spans. Both
      statistics attract non-negligible rates of false positives. Using a
      frequency floor will eliminate many OCR errors, but does not reduce the
      rates of MI and LL false positives.
      
      Assessing the potential of two post-OCR correction methods, I find that
      VARD, a program designed to standardise natural spelling variation, proves
      unpromising for dealing with OCR errors. By contrast, Overproof, a
      commercial system designed for OCR errors, is effective, and its
      application leads to substantial improvements in the reliability of MI and
      LL, particularly for large spans.
      
      The second set of issues relate to the effectiveness of approaches to
      analysing the discourses surrounding place-names in digitised
      nineteenth-century newspapers. I single out three approaches to
      identifying place-names mentioned in large amounts of text without the
      need for a geo-parser system. The first involves relying on USAS, a
      semantic tagger, which has a 'Z2' tag for geographic names. This
      approach cannot identify multi-word place-names, but is scalable. A
      difficulty is that frequency counts of place-names do not account for
      their possible polysemy; I suggest a procedure involving reading a random
      sample of concordance lines for each place-name, in order to obtain an
      estimate of the actual number of mentions of that place-name in reference
      to a specific place. This method is best used to identify the most
      frequent place-names. A second, related, approach is to automatically
      compare a list of words tagged 'Z2' with a gazetteer, a
      reference list of place-names. This method, however, suffers from the same
      difficulties as the previous one, and is best used when accurate frequency
      counts are not required. A third approach involves starting from a
      principled, text-external, list of place-names, such as a population
      table, then attempting to locate each place in the set of texts. The
      scalability of this method depends on the length of the list of
      place-names, but it can accommodate any quantity of text. Its advantage
      over the two other methods is that it helps to contextualise the findings
      and can help identify place-names which are not mentioned in the texts.
      
      Finally, I consider two approaches to investigating the discourses
      surrounding place-names in large quantities of text. Both are scalable
      operationalisations of proximity-based collocation. The first approach
      starts with the whole corpus, searching for the place-name of interest and
      generating a list of statistical collocates of the place-name; these
      collocates can then be further categorised and analysed via concordance
      analysis. The second approach starts with small samples of concordance
      lines for the place-name of interest, and involves analysing these
      concordance lines to develop a framework for description of the
      phraseologies within which place-names are mentioned. Both methods are
      useful and scalable; the findings they yield are, to some extent,
      overlapping, but also complementary. This suggests that both methods may
      be fruitfully used together, albeit neither is ideally-suited for
      comparing results across corpora. Both approaches are well-suited for
      exploratory research.