Abstract
The increasing availability of digitised registration records presents a significant opportunity for research in many fields including those of human geography, genealogy and medicine. Re-examining original records allows researchers to study relationships between factors such as occupation, cause of death, illness and geographic region. This can be facilitated by coding these factors to standard classifications. This chapter describes work to develop a method for automatically coding the occupations from 29 million Scottish birth, death and marriage records, containing around 50 million occupation descriptions, to standard classifications. A range of approaches using text processing and supervised machine learning is evaluated, achieving classification performance of 75 % micro-precision/recall, 61 % macro-precision and 66 % macro-recall on a smaller test set. Further development that may be needed for classification of the full data set is discussed.
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Notes
- 1.
The 50 % threshold was arbitrarily chosen; the effect of varying this may be investigated in future work.
- 2.
For the string similarity classifier, which does not involve any training, these records were ignored.
- 3.
Where a record had been originally multiple-coded, one of the codes was arbitrarily selected as ‘correct’.
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Acknowledgments
This work was supported by the Economic and Social Research Council grant numbers ES/K00574X/1 (Digitising Scotland), ES/L007487/1 (Administrative Data Research Centre—Scotland).
This chapter is an expanded version of a paper presented at the International Workshop on Population Reconstruction in Amsterdam, February 2014 (Kirby et al. 2014). We thank the original anonymous referee, many workshop participants and the book editors, for their helpful comments.
We thank Richard Zijdeman for advice on historical coding and access to data from the Netherlands.
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Kirby, G. et al. (2015). Automatic Methods for Coding Historical Occupation Descriptions to Standard Classifications. In: Bloothooft, G., Christen, P., Mandemakers, K., Schraagen, M. (eds) Population Reconstruction. Springer, Cham. https://doi.org/10.1007/978-3-319-19884-2_3
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