Automatic methods for coding historical occupation descriptions to standard classifications
Population Reconstruction, 2015•Springer
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 …
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 …
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.
Springer
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