Learning Job Titles Similarity from Noisy Skill Labels

Rabih Zbib, Lucas Alvarez Lacasa, Federico Retyk, Rus Poves, Juan Aizpuru +3 more
7/1/2022
cs.IRcs.AI

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Abstract

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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Cite this paper

@article{zbib2022learning,
  title  = {Learning Job Titles Similarity from Noisy Skill Labels},
  author = {Rabih Zbib and Lucas Alvarez Lacasa and Federico Retyk and Rus Poves and Juan Aizpuru and Hermenegildo Fabregat and Vaidotas Simkus and Emilia García-Casademont},
  year   = {2022},
  eprint = {2207.00494},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2207.00494v3}
}

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