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Learning Job Titles Similarity from Noisy Skill Labels

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arxiv 2207.00494 v3 pith:H5KRPULB submitted 2022-07-01 cs.IR cs.AI

Rabih Zbib , Lucas Alvarez Lacasa , Federico Retyk , Rus Poves , Juan Aizpuru , Hermenegildo Fabregat , Vaidotas Simkus , Emilia García-Casademont This is my paper
classification cs.IRcs.AI
keywords learningsimilaritylabelsnoisyskilltitletitlestraining
verification ladder T0 review T1 audit T2 compute T3 formal
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Measuring semantic similarity between job titles is an essential functionality for automatic job recommendations. This task is usually approached using supervised learning techniques, which requires training data in the form of equivalent job title pairs. In this paper, we instead propose an unsupervised representation learning method for training a job title similarity model using noisy skill labels. We show that it is highly effective for tasks such as text ranking and job normalization.

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Cited by 4 Pith papers

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

  1. Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphs

    cs.CL 2025-09 conditional novelty 6.0 of 10

    A self-supervised pipeline that pairs fine-tuned SBERT with a skill knowledge graph reduces RMSE for highly related job title pairs to 0.11, about 25% to 39% below strong baselines.

  2. Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management

    cs.CL 2025-07 conditional novelty 5.0 of 10

    TalentCLEF 2025 publishes the first public multilingual benchmark for job title matching and skill prediction, with results showing training strategy matters more than model size.

  3. 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.

  4. 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.

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