# Towards a Job Title Classification System

arXiv: https://arxiv.org/abs/1606.00917 · HTML: https://mlsanity.com/paper/1606.00917/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2016.

## Built on by (most cited first, 3 of 3)

- [ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain](https://mlsanity.com/paper/2305.12092/) (2023)
- [Generative AI Impact on Labor Market: Analyzing ChatGPT's Demand in Job Advertisements](https://mlsanity.com/paper/2412.07042/) (2024)
- [NLPnorth @ TalentCLEF 2025: Comparing Discriminative, Contrastive, and Prompt-Based Methods for Job Title and Skill Matching](https://mlsanity.com/paper/2506.19058/) (2025)

## Abstract (arXiv)

Document classification for text, images and other applicable entities has long been a focus of research in academia and also finds application in many industrial settings. Amidst a plethora of approaches to solve such problems, machine-learning techniques have found success in a variety of scenarios. In this paper we discuss the design of a machine learning-based semi-supervised job title classification system for the online job recruitment domain currently in production at CareerBuilder.com and propose enhancements to it. The system leverages a varied collection of classification as well clustering algorithms. These algorithms are encompassed in an architecture that facilitates leveraging existing off-the-shelf machine learning tools and techniques while keeping into consideration the challenges of constructing a scalable classification system for a large taxonomy of categories. As a continuously evolving system that is still under development we first discuss the existing semi-supervised classification system which is composed of both clustering and classification components in a proximity-based classifier setup and results of which are already used across numerous products at CareerBuilder. We then elucidate our long-term goals for job title classification and propose enhancements to the existing system in the form of a two-stage coarse and fine level classifier augmentation to construct a cascade of hierarchical vertical classifiers. Preliminary results are presented using experimental evaluation on real world industrial data.