# Salience and Market-aware Skill Extraction for Job Targeting

arXiv: https://arxiv.org/abs/2005.13094 · HTML: https://mlsanity.com/paper/2005.13094/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2020.

## Built on by (most cited first, 12 of 12)

- [SkillSpan: Hard and Soft Skill Extraction from English Job Postings](https://mlsanity.com/paper/2204.12811/) (2022)
- [ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain](https://mlsanity.com/paper/2305.12092/) (2023)
- [Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings](https://mlsanity.com/paper/2402.05617/) (2024)
- [Rethinking Skill Extraction in the Job Market Domain using Large Language Models](https://mlsanity.com/paper/2402.03832/) (2024)
- [Natural Language Processing for Human Resources: A Survey](https://mlsanity.com/paper/2410.16498/) (2024)
- [NNOSE: Nearest Neighbor Occupational Skill Extraction](https://mlsanity.com/paper/2401.17092/) (2024)
- [JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning](https://mlsanity.com/paper/2202.10739/) (2022)
- [Global Context Enhanced Social Recommendation with Hierarchical Graph Neural Networks](https://mlsanity.com/paper/2110.04039/) (2021)
- [Fake Resume Attacks: Data Poisoning on Online Job Platforms](https://mlsanity.com/paper/2402.14124/) (2024)
- [Scalable Detection of Salient Entities in News Articles](https://mlsanity.com/paper/2405.20461/) (2024)
- [CAPER: Enhancing Career Trajectory Prediction using Temporal Knowledge Graph and Ternary Relationship](https://mlsanity.com/paper/2408.15620/) (2024)
- [Convergence-aware Clustered Federated Graph Learning Framework for Collaborative Inter-company Labor Market Forecasting](https://mlsanity.com/paper/2409.19545/) (2024)

## Abstract (arXiv)

At LinkedIn, we want to create economic opportunity for everyone in the global workforce. To make this happen, LinkedIn offers a reactive Job Search system, and a proactive Jobs You May Be Interested In (JYMBII) system to match the best candidates with their dream jobs. One of the most challenging tasks for developing these systems is to properly extract important skill entities from job postings and then target members with matched attributes. In this work, we show that the commonly used text-based salience and market-agnostic skill extraction approach is sub-optimal because it only considers skill mention and ignores the salient level of a skill and its market dynamics, i.e., the market supply and demand influence on the importance of skills. To address the above drawbacks, we present model, our deployed salience and market-aware skill extraction system. The proposed model ~shows promising results in improving the online performance of job recommendation (JYMBII) (+1.92% job apply) and skill suggestions for job posters (-37% suggestion rejection rate). Lastly, we present case studies to show interesting insights that contrast traditional skill recognition method and the proposed model~from occupation, industry, country, and individual skill levels. Based on the above promising results, we deployed the model ~online to extract job targeting skills for all 20M job postings served at LinkedIn.