Improving Clustering on Occupational Text Data through Dimensionality Reduction

2025/07/10 by García, Iago Xabier Vázquez, Damla Partanaz, E. Fatih Yetkin +2
Business, Management and Accounting · Computer Science · Health Professions · #AI and HR Technologies #Cluster analysis #Clustering high-dimensional data #Computation and Language (cs.CL) #Computers and Society (cs.CY) #Curse of dimensionality #Dimensionality reduction #FOS: Computer and information sciences #Information Systems Education and Curriculum Development #Machine Learning (cs.LG) #Occupational Therapy Practice and Research #Pipeline (software) #Reduction (mathematics) #Silhouette

paper · pdf · doi:10.48550/arxiv.2507.07582

published in arXiv (Cornell University) (Cornell University)

openalex publication_date 2025/07/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

In this study, we focused on proposing an optimal clustering mechanism for the occupations defined in the well-known US-based occupational database, O*NET. Even though all occupations are defined according to well-conducted surveys in the US, their definitions can vary for different firms and countries. Hence, if one wants to expand the data that is already collected in O*NET for the occupations defined with different tasks, a map between the definitions will be a vital requirement. We proposed a pipeline using several BERT-based techniques with various clustering approaches to obtain such a map. We also examined the effect of dimensionality reduction approaches on several metrics used in measuring performance of clustering algorithms. Finally, we improved our results by using a specialized silhouette approach. This new clustering-based mapping approach with dimensionality reduction may help distinguish the occupations automatically, creating new paths for people wanting to change their careers.

Citations

• Using contextual sentence analysis models to recognize ESG concepts
• Exploring Dimensionality Reduction Techniques in Multilingual Transformers
• Evaluation of BERT and ALBERT Sentence Embedding Performance on Downstream NLP Tasks
• IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News Articles
• MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
• DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
• Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
• Deep contextualized word representations
• The Growing Importance of Social Skills in the Labor Market*
• Attention Is All You Need
• Efficient Estimation of Word Representations in Vector Space
• Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Related

• IPOD: An Industrial and Professional Occupations Dataset and its\n Applications to Occupational Data Mining and Analysis
• A Large-scale Industrial and Professional Occupation Dataset
• Standard Occupation Classifier -- A Natural Language Processing Approach
• NNOSE: Nearest Neighbor Occupational Skill Extraction
• occ2vec: A principal approach to representing occupations using natural language processing