A practical method for occupational skills detection in Vietnamese job listings

TL;DR

This paper presents a practical, ranking-based method for detecting occupational skills in Vietnamese job listings, addressing data scarcity issues and outperforming traditional NER approaches.

Contribution

It introduces a novel ranking-based pipeline for skill detection that reduces reliance on large annotated datasets and improves accuracy in scarce data scenarios.

Findings

The proposed method outperforms NER models on limited datasets.

The pipeline effectively extracts and ranks skill phrases in context.

Experiments on three datasets validate the approach's robustness.

Abstract

Vietnamese labor market has been under an imbalanced development. The number of university graduates is growing, but so is the unemployment rate. This situation is often caused by the lack of accurate and timely labor market information, which leads to skill miss-matches between worker supply and the actual market demands. To build a data monitoring and analytic platform for the labor market, one of the main challenges is to be able to automatically detect occupational skills from labor-related data, such as resumes and job listings. Traditional approaches rely on existing taxonomy and/or large annotated data to build Named Entity Recognition (NER) models. They are expensive and require huge manual efforts. In this paper, we propose a practical methodology for skill detection in Vietnamese job listings. Rather than viewing the task as a NER task, we consider the task as a ranking problem. We propose a pipeline in which phrases are first extracted and ranked in semantic similarity with the phrases' contexts. Then we employ a final classification to detect skill phrases. We collected three datasets and conducted extensive experiments. The results demonstrated that our methodology achieved better performance than a NER model in scarce datasets.