Enhancing Job Matching: Occupation, Skill and Qualification Linking with the ESCO and EQF taxonomies

• URL: http://arxiv.org/abs/2512.03195v1
• Date: Tue, 02 Dec 2025 19:49:43 GMT
• Title: Enhancing Job Matching: Occupation, Skill and Qualification Linking with the ESCO and EQF taxonomies
• Authors: Stylianos Saroglou, Konstantinos Diamantaras, Francesco Preta, Marina Delianidi, Apostolos Benisis, Christian Johannes Meyer,
• Abstract summary: This study investigates the potential of language models to improve the classification of labor market information.<n>We examine and compare two prominent methodologies from the literature: Sentence Linking and Entity Linking.<n>In support of ongoing research, we release an open-source tool, incorporating these two methodologies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study investigates the potential of language models to improve the classification of labor market information by linking job vacancy texts to two major European frameworks: the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy and the European Qualifications Framework (EQF). We examine and compare two prominent methodologies from the literature: Sentence Linking and Entity Linking. In support of ongoing research, we release an open-source tool, incorporating these two methodologies, designed to facilitate further work on labor classification and employment discourse. To move beyond surface-level skill extraction, we introduce two annotated datasets specifically aimed at evaluating how occupations and qualifications are represented within job vacancy texts. Additionally, we examine different ways to utilize generative large language models for this task. Our findings contribute to advancing the state of the art in job entity extraction and offer computational infrastructure for examining work, skills, and labor market narratives in a digitally mediated economy. Our code is made publicly available: https://github.com/tabiya-tech/tabiya-livelihoods-classifier

Related papers

• UniSkill: A Dataset for Matching University Curricula to Professional Competencies [3.9445288162247483]
  We release both manually annotated and synthetic datasets of skills from the European Skills, Competences, Qualifications and Occupations taxonomy.<n>We match graduate-level university courses with skills from the Systems Analysts and Management and Organization Analyst ESCO occupation groups at two granularities.<n>We train language models on this dataset to serve as a baseline for retrieval and recommendation systems for course-to-skill and skill-to-course matching.
  arXiv  Detail & Related papers  (2026-03-03T16:05:57Z)
• Standard Occupation Classifier -- A Natural Language Processing Approach [0.0]
  This project investigates the use of recent developments in natural language processing to construct a classifier capable of assigning an occupation code to a given job advertisement.<n>We develop various classifiers for both UK ONS SOC and US O*NET SOC, using different Language Models.<n>We find that an ensemble model, which combines Google BERT and a Neural Network classifier while considering job title, description, and skills, achieved the highest prediction accuracy.
  arXiv  Detail & Related papers  (2025-11-28T10:30:37Z)
• Overview of the TalentCLEF 2025: Skill and Job Title Intelligence for Human Capital Management [0.2276267460638319]
  We present TalentCLEF 2025, the first evaluation campaign focused on skill and job title intelligence.<n>The evaluations included monolingual and cross-lingual scenarios and covered the evaluation of gender bias.<n> TalentCLEF provides the first public benchmark in this field and encourages the development of robust, fair, and transferable language technologies for the labor market.
  arXiv  Detail & Related papers  (2025-07-17T16:33:57Z)
• Tec-Habilidad: Skill Classification for Bridging Education and Employment [0.7373617024876725]
  This paper develops a Spanish language dataset for skill extraction and classification.<n>It provides annotation methodology to distinguish between knowledge, skill, and abilities.<n>It also provides deep learning baselines to advance robust solutions for skill classification.
  arXiv  Detail & Related papers  (2025-03-05T22:05:42Z)
• Joint Extraction and Classification of Danish Competences for Job Matching [13.364545674944825]
  This work presents the first model that jointly extracts and classifies competence from Danish job postings. As a single BERT-like architecture for joint extraction and classification, our model is lightweight and efficient at inference.
  arXiv  Detail & Related papers  (2024-10-29T15:00:40Z)
• Understanding Cross-Lingual Alignment -- A Survey [52.572071017877704]
  Cross-lingual alignment is the meaningful similarity of representations across languages in multilingual language models. We survey the literature of techniques to improve cross-lingual alignment, providing a taxonomy of methods and summarising insights from throughout the field.
  arXiv  Detail & Related papers  (2024-04-09T11:39:53Z)
• Hierarchical Classification of Transversal Skills in Job Ads Based on Sentence Embeddings [0.0]
  This paper aims to identify correlations between job ad requirements and skill sets using a deep learning model. The approach involves data collection, preprocessing, and labeling using ESCO (European Skills, Competences, and Occupations) taxonomy.
  arXiv  Detail & Related papers  (2024-01-10T11:07:32Z)
• Cross-Lingual NER for Financial Transaction Data in Low-Resource Languages [70.25418443146435]
  We propose an efficient modeling framework for cross-lingual named entity recognition in semi-structured text data. We employ two independent datasets of SMSs in English and Arabic, each carrying semi-structured banking transaction information. With access to only 30 labeled samples, our model can generalize the recognition of merchants, amounts, and other fields from English to Arabic.
  arXiv  Detail & Related papers  (2023-07-16T00:45:42Z)
• Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification [58.720142291102135]
  This case study investigates the task of job classification in a real-world setting. The goal is to determine whether an English-language job posting is appropriate for a graduate or entry-level position.
  arXiv  Detail & Related papers  (2023-03-13T14:09:53Z)
• ERICA: Improving Entity and Relation Understanding for Pre-trained Language Models via Contrastive Learning [97.10875695679499]
  We propose a novel contrastive learning framework named ERICA in pre-training phase to obtain a deeper understanding of the entities and their relations in text. Experimental results demonstrate that our proposed ERICA framework achieves consistent improvements on several document-level language understanding tasks.
  arXiv  Detail & Related papers  (2020-12-30T03:35:22Z)
• CoLAKE: Contextualized Language and Knowledge Embedding [81.90416952762803]
  We propose the Contextualized Language and Knowledge Embedding (CoLAKE) CoLAKE jointly learns contextualized representation for both language and knowledge with the extended objective. We conduct experiments on knowledge-driven tasks, knowledge probing tasks, and language understanding tasks.
  arXiv  Detail & Related papers  (2020-10-01T11:39:32Z)
• Explaining Relationships Between Scientific Documents [55.23390424044378]
  We address the task of explaining relationships between two scientific documents using natural language text. In this paper we establish a dataset of 622K examples from 154K documents.
  arXiv  Detail & Related papers  (2020-02-02T03:54:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.