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JobBERT: Understanding Job Titles through Skills

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arxiv 2109.09605 v1 pith:AOPST66J submitted 2021-09-20 cs.CL

Jens-Joris Decorte , Jeroen Van Hautte , Thomas Demeester , Chris Develder This is my paper
classification cs.CL
keywords titlesjobbertmanymodelprocessesthemaccuracyallow
verification ladder T0 review T1 audit T2 compute T3 formal
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Job titles form a cornerstone of today's human resources (HR) processes. Within online recruitment, they allow candidates to understand the contents of a vacancy at a glance, while internal HR departments use them to organize and structure many of their processes. As job titles are a compact, convenient, and readily available data source, modeling them with high accuracy can greatly benefit many HR tech applications. In this paper, we propose a neural representation model for job titles, by augmenting a pre-trained language model with co-occurrence information from skill labels extracted from vacancies. Our JobBERT method leads to considerable improvements compared to using generic sentence encoders, for the task of job title normalization, for which we release a new evaluation benchmark.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Noise is Signal: Density-Based Outliers as Leading Indicators of Occupational Emergence in Labor Market Text

    cs.LG 2026-06 unverdicted novelty 7.0 of 10

    Density-based outliers in labor market text act as leading indicators of new occupational clusters, with an extended Emerging Occupation Score predicting formation 2 quarters ahead at F1=0.74 on 84,988 postings.

  2. Synthetic CVs To Build and Test Fairness-Aware Hiring Tools

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    A new synthetic CV dataset, generated from donated real CVs, is proposed as a benchmark for fairness-aware algorithmic hiring research.

  3. Unified Semantic Modeling Framework for Large-Scale Job Understanding at LinkedIn

    cs.AI 2026-06 conditional novelty 5.0 of 10

    A GPT-4-distilled small LM plus grouped LoRA adapters improves LinkedIn's job-attribute classification over legacy models.

  4. TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Management

    cs.CL 2026-07 unverdicted novelty 4.0 of 10

    TalentCLEF 2026 will run two shared tasks—job-person matching and job-skill matching with skill type labels—on synthetic multilingual hiring data, but the paper presents no results.

  5. Ontology-Aligned Embeddings for Data-Driven Labour Market Analytics

    cs.LG 2025-09 conditional novelty 4.0 of 10

    Fine-tuning Sentence-BERT on KldB-coded job titles yields high-accuracy German occupation classification via semantic k-NN search, with a rule-based ISCED education overlay.

  6. Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Management

    cs.CL 2026-06 unverdicted novelty 3.0 of 10

    The paper describes the organization, tasks, datasets, and participation results for the TalentCLEF 2026 challenge, which received 113 team registrations and over 400 submissions.

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