Cross-domain Transfer Learning for Recognizing Professional Skills from Chinese Job Postings

[Objective] This paper analyzes the online job postings and identifies the demands of employers accurately, aiming to address the skill gaps between supply and demand in the labor market. [Methods] We proposed a model with cross-domain transfer learning to recognize professional skill words (CDTL-PSE). This task was treated as sequence tagging like named entity recognition or term extraction in CDTL-PSE. It also decomposed the SIGHAN corpus into three source domains. A domain adaptation layer was inserted between the Bi-LSTM and the CRF layers, which helped us transfer learning from each source domain to the target domain. Then, we used parameter transfer approach to train each sub-model. Finally, we obtained the prediction of label sequence by majority vote. [Results] On the self-built online recruitment data set, compared with the baseline method, the proposed model improved the F1 value by 0.91%, and reduced the labeled samples by about 50%. [Limitations] The interpretability of CDTL-PSE needs to be further improved. [Conclusions] CDTL-PSE can automatically extract words on professional skills, and effectively increase the labeled samples in the target domain. © 2022, Chinese Academy of Sciences. All rights reserved.