Automatic methods for coding historical occupation descriptions to standard classifications

Graham Njal Cameron Kirby, Jamie Kirk Carson, Fraser Robin James Dunlop, Christopher John Lloyd Dibben, Alan Dearle, Lee Williamson, Eilidh Garrett, Alice Reid

Research output: Contribution to conferencePaperpeer-review


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 paper 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 accuracy of 92.3 ± 0.2% on a smaller test set. The paper speculates on further development that may be needed for classification of the full data set.
Original languageEnglish
Publication statusAccepted/In press - 2014
EventWorkshop on Population Reconstruction - International Institute of Social History, Amsterdam, Netherlands
Duration: 19 Feb 201421 Feb 2014


WorkshopWorkshop on Population Reconstruction


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