TY - GEN
T1 - Word embedding for job market spatial representation
T2 - 8th IEEE International Conference on Big Data, Big Data 2020
AU - Jaramillo, Catalina M.
AU - Squires, Paul
AU - Kaufman, Harold G.
AU - Mendes Da Silva, Andre
AU - Togelius, Julian
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/10
Y1 - 2020/12/10
N2 - 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.
AB - 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.
KW - NLP
KW - employment
KW - job market
KW - skills
KW - transformers
KW - word embeddings
UR - http://www.scopus.com/inward/record.url?scp=85103849778&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103849778&partnerID=8YFLogxK
U2 - 10.1109/BigData50022.2020.9377850
DO - 10.1109/BigData50022.2020.9377850
M3 - Conference contribution
AN - SCOPUS:85103849778
T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
SP - 5713
EP - 5715
BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
A2 - Wu, Xintao
A2 - Jermaine, Chris
A2 - Xiong, Li
A2 - Hu, Xiaohua Tony
A2 - Kotevska, Olivera
A2 - Lu, Siyuan
A2 - Xu, Weijia
A2 - Aluru, Srinivas
A2 - Zhai, Chengxiang
A2 - Al-Masri, Eyhab
A2 - Chen, Zhiyuan
A2 - Saltz, Jeff
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 10 December 2020 through 13 December 2020
ER -