labourR 1.0.0: Automatic Coding of Occupation Titles

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Occupations classification is an important step in tasks such as labour market analysis, epidemiological studies and official statistics. To assist research on the labour market, ESCO has defined a taxonomy for occupations. Occupations are specified and organized in a hierarchical structure based on the International Standard Classification of Occupations (ISCO). labourR is a new package that performs occupations coding for multilingual free-form text (e.g. a job title) using the ESCO hierarchical classification model.

The initial motivation was to retrieve the work experience history from a Curriculum Vitae generated from the Europass online CV editor. Document vectorization is performed using the ESCO model and a fuzzy match is allowed with various string distance metrics.

The labourR package:

  • Allows classifying multilingual free-text using the ESCO-ISCO hierarchy of occupations.
  • Computations are fully vectorized and memory efficient.
  • Includes facilities to assist research in information mining of labour market data.

Installation

You can install the released version of labourR from CRAN with,

install.packages("labourR")

Example

    library(labourR)
    corpus <- data.frame(
      id = 1:3,
      text = c("Data Scientist", "Junior Architect Engineer", "Cashier at McDonald's")
    )

    Given an ISCO level, the top suggested ISCO group is returned. num_leaves specifies the number of ESCO occupations used to perform a plurality vote,

    classify_occupation(corpus = corpus, isco_level = 3, lang = "en", num_leaves = 5)
    #>    id iscoGroup                                          preferredLabel
    #> 1:  1       251       Software and applications developers and analysts
    #> 2:  2       214 Engineering professionals (excluding electrotechnology)
    #> 3:  3       523                              Cashiers and ticket clerks
    

    For further information browse the vignettes.


    labourR 1.0.0: Automatic Coding of Occupation Titles was first posted on July 24, 2020 at 7:07 am.
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