Semantic Similarity Strategies for Job Title Classification

TL;DR

This paper explores semantic representation strategies like word vectors and document similarity measures to enhance the accuracy of large-scale job title classification systems, which are vital for various online recruitment applications.

Contribution

It introduces experiments with semantic strategies such as W2V vectors and WMD to improve a two-stage job title classification system.

Findings

Semantic strategies improve classification accuracy

WMD enhances document similarity measurement

Two-stage classifier benefits from semantic enhancements

Abstract

Automatic and accurate classification of items enables numerous downstream applications in many domains. These applications can range from faceted browsing of items to product recommendations and big data analytics. In the online recruitment domain, we refer to classifying job ads to pre-defined or custom occupation categories as job title classification. A large-scale job title classification system can power various downstream applications such as semantic search, job recommendations and labor market analytics. In this paper, we discuss experiments conducted to improve our in-house job title classification system. The classification component of the system is composed of a two-stage coarse and fine level classifier cascade that classifies input text such as job title and/or job ads to one of the thousands of job titles in our taxonomy. To improve classification accuracy and effectiveness, we experiment with various semantic representation strategies such as average W2V vectors and document similarity measures such as Word Movers Distance (WMD). Our initial results show an overall improvement in accuracy of Carotene[1].