mlsanityData to 7 Oct 2026

Learning Job Titles Similarity from Noisy Skill Labels

arXiv 2207.00494 ↗First posted 1 Jul 2022cs.IR cs.AIcited by 12

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Abstract (arXiv)

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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 11 of its 19 references that are in the map. First posted on arXiv 1 Jul 2022.

Builds on

11 of 19 references.

References · 11

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  3. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  4. 2018SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processingcited by 1,973
  5. 2014Distributed Representations of Sentences and Documentscited by 677
  6. 2020Pretrained Transformers for Text Ranking: BERT and Beyondcited by 266
  7. 2019Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoringcited by 164
  8. 2015Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrievalcited by 54
  9. 2017Neural Models for Information Retrievalcited by 29
  10. 2021JobBERT: Understanding Job Titles through Skillscited by 29
  11. 2020Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learningcited by 16

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 6; 2026: 3.

12 papers.

  1. 2023A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analyticscited by 17
  2. 2023VacancySBERT: the approach for representation of titles and skills for semantic similarity search in the recruitment domaincited by 7
  3. 2025Efficient Text Encoders for Labor Market Analysiscited by 5
  4. 2025Multilingual JobBERT for Cross-Lingual Job Title Matchingcited by 4
  5. 2024Learning Job Title Representation from Job Description Aggregation Networkcited by 3
  6. 2025Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Managementcited by 3
  7. 2025Measuring Gender Bias in Job Title Matching for Grammatical Gender Languagescited by 2
  8. 2026WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domaincited by 1
  9. 2025Ontology-Aligned Embeddings for Data-Driven Labour Market Analyticscited by 0
  10. 2025Towards Explainable Job Title Matching: Leveraging Semantic Textual Relatedness and Knowledge Graphscited by 0
  11. 2026Overview of the TalentCLEF 2026: Skill and Job Title Intelligence for Human Capital Managementcited by 0
  12. 2026TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Managementcited by 0