Predicting Job Titles from Job Descriptions with Multi-label Text Classification

TL;DR

This paper introduces a multi-label classification method using Bi-GRU-LSTM-CNN and pre-trained language models, notably BERT, to predict relevant job titles from job descriptions, achieving significant F1-score improvements.

Contribution

It presents a novel multi-label classification framework with various pre-trained models for job title prediction from text descriptions.

Findings

BERT with multilingual pre-trained model achieved the highest F1-score.

F1-score of 62.20% on development set and 47.44% on test set.

The proposed approach outperforms baseline methods.

Abstract

Finding a suitable job and hunting for eligible candidates are important to job seeking and human resource agencies. With the vast information about job descriptions, employees and employers need assistance to automatically detect job titles based on job description texts. In this paper, we propose the multi-label classification approach for predicting relevant job titles from job description texts, and implement the Bi-GRU-LSTM-CNN with different pre-trained language models to apply for the job titles prediction problem. The BERT with multilingual pre-trained model obtains the highest result by F1-scores on both development and test sets, which are 62.20% on the development set, and 47.44% on the test set.