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Automatically Coding Occupation Titles to a Standard Occupation Classification

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dc.contributor.advisor Song, Fei
dc.contributor.advisor Grewal, Gary Nahoomi, Negin 2018-09-05T18:42:47Z 2018-09-05T18:42:47Z 2018-09 2018-08-31 2018-09-05
dc.description.abstract Occupation Coding is the process of classifying job titles into one or multiple categories that are usually organized into a hierarchy. Historically, the task of classifying job titles to standard classifications was done manually. However, the drawbacks of manual coding have led researchers to develop automatic methods for occupation coding. We compare the classic machine learning approaches and the deep learning approaches on classifying job titles to Standard Occupational Classification (SOC). We implement flat and hierarchical models using Naïve Bayes, Maximum Entropy (MaxEnt), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) to code job titles to SOC. For this purpose, 65,962 SOC labeled job titles are collected from publicly available sources. These job titles are extremely short with an average of three words per job title. Our experimental results show that MaxEnt, SVM, and CNN perform similarly and are better than Naïve Bayes on coding job titles to SOC. en_US
dc.language.iso en en_US
dc.rights Attribution-NoDerivs 2.5 Canada *
dc.rights.uri *
dc.subject automatic occupation coding en_US
dc.subject multi-label classification en_US
dc.subject hierarchical classification en_US
dc.subject short text classification en_US
dc.subject machine learning en_US
dc.subject deep learning en_US
dc.subject convolutional neural network en_US
dc.subject svm en_US
dc.subject maximum entropy en_US
dc.subject naive bayes en_US
dc.title Automatically Coding Occupation Titles to a Standard Occupation Classification en_US
dc.type Thesis en_US Computer Science en_US Master of Science en_US School of Computer Science en_US
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Attribution-NoDerivs 2.5 Canada Except where otherwise noted, this item's license is described as Attribution-NoDerivs 2.5 Canada