VEUCTOR: Training and selecting best vector space models from online job ads for European countries

Over the last decade, word embeddings have enabled machines to represent words and sentences as vectors, enabling researchers to reason on text for tasks like semantic similarity, contextual understanding, machine translation, etc. However, the synthesis of embeddings involves domain-specific parameters that affect semantic accuracy and contextual relevance, often leading to unpredictable biases and inconsistent comparisons. This issue is particularly relevant in labor market analysis, where different embeddings yield varying results, making the selection of the most appropriate model a key element. This paper addresses these challenges by (i) proposing a methodology to train, select, and align vector space models for a target taxonomy, ensuring comparability across dimensions and languages; (ii) applying this approach to 4.5 million job ads in 28 languages, aligning country-specific embeddings using the ESCO taxonomy; (iii) generating over 3000 models over 142 machine days, making the best-performing ones publicly available via VEUCTOR; and (iv) showing how model choice significantly impacts labor market analysis, revealing substantial variations in occupational skill bundles across embeddings.