{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-a-job-title-classification-system","title":"Towards a Job Title Classification System","arxiv_id":"1606.00917","date":"2016-06-02","proceeding":null,"authors":["Faizan Javed","Matt McNair","Ferosh Jacob","Meng Zhao"],"abstract":"Document classification for text, images and other applicable entities has\nlong been a focus of research in academia and also finds application in many\nindustrial settings. Amidst a plethora of approaches to solve such problems,\nmachine-learning techniques have found success in a variety of scenarios. In\nthis paper we discuss the design of a machine learning-based semi-supervised\njob title classification system for the online job recruitment domain currently\nin production at CareerBuilder.com and propose enhancements to it. The system\nleverages a varied collection of classification as well clustering algorithms.\nThese algorithms are encompassed in an architecture that facilitates leveraging\nexisting off-the-shelf machine learning tools and techniques while keeping into\nconsideration the challenges of constructing a scalable classification system\nfor a large taxonomy of categories. As a continuously evolving system that is\nstill under development we first discuss the existing semi-supervised\nclassification system which is composed of both clustering and classification\ncomponents in a proximity-based classifier setup and results of which are\nalready used across numerous products at CareerBuilder. We then elucidate our\nlong-term goals for job title classification and propose enhancements to the\nexisting system in the form of a two-stage coarse and fine level classifier\naugmentation to construct a cascade of hierarchical vertical classifiers.\nPreliminary results are presented using experimental evaluation on real world\nindustrial data.","url_abs":"http://arxiv.org/abs/1606.00917v1","url_pdf":"http://arxiv.org/pdf/1606.00917v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"towards-a-job-title-classification-system","repo_url":"https://github.com/kmamykin/askamanager_salary_survey","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"document-classification","task_name":"Document Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}