Hierarchy-Aware Heterogeneous Graph Neural Network for Occupation Title Coding

Occupation coding encodes job titles into standard occupation labels, which is effective for data processing but tedious. Research proves that classic machine learning is effective, but accuracy needs further improvement. We construct a real data set with 881 occupation categories, including 41,297 pairs of job titles and corresponding labels. We design a hierarchy-aware heterogeneous graph neural network, combining prior knowledge from occupation category trees and synonyms. Results show our model outperforms other methods by 7.62% on micro-F1. It also alleviates the dependence on data as it achieves 52.28% on micro-F1 with only 30% of the original training data set.