Word embedding for job market spatial representation: Tracking changes and predicting skills demand

Catalina M. Jaramillo, Paul Squires, Harold G. Kaufman, Andre Mendes Da Silva, Julian Togelius

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    What will the job market of the future look like? What jobs will be popular, and which skills will they require? Modeling the temporal progression of the job market, as represented by job ads, may help us answer this question. This paper represents a first step in this direction. In order to build a spatial representation of job market that allows to track changes in skills' demand, authors are training models to classify job tasks. Different natural language processing and classification approaches were compared, including term frequency - inverse document frequency, principal components analysis, word2vec, GloVe, fastText and BERT models, and feedforward neural networks, support vector machines, and bidirectional long short term memory recurrent neural networks. BERT obtained the best accuracy results with 52% for 94 classes and 65% for 22 classes.

    Original languageEnglish (US)
    Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
    EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages5713-5715
    Number of pages3
    ISBN (Electronic)9781728162515
    DOIs
    StatePublished - Dec 10 2020
    Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
    Duration: Dec 10 2020Dec 13 2020

    Publication series

    NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

    Conference

    Conference8th IEEE International Conference on Big Data, Big Data 2020
    CountryUnited States
    CityVirtual, Atlanta
    Period12/10/2012/13/20

    Keywords

    • employment
    • job market
    • NLP
    • skills
    • transformers
    • word embeddings

    ASJC Scopus subject areas

    • Computer Networks and Communications
    • Information Systems
    • Information Systems and Management
    • Safety, Risk, Reliability and Quality

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