Abstract
This article describes a new method for analyzing labor market requirements by matching job listings from online recruitment platforms with professional standards to weigh the importance of particular professional functions and requirements and enrich the general concepts of professional standards using real labor market requirements. Our approach aims to combat the gap between professional standards and reality of fast changing requirements in developing branches of economy. First, we determine professions for each job description, using the multi-label classifier based on convolutional neural networks. Secondly, we solve the task of concept matching between job descriptions and standards for the respective professions by applying distributional semantic models. In this task, the average word2vec model achieved the best performance among other vector space models. Finally, we experiment with expanding general vocabulary of professional standards with the most frequent unigrams and bigrams occurring in matching job descriptions. Performance evaluation is carried out on a representative corpus of job listings and professional standards in the field of IT.
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Gorshkov, M.K., Kliucharev, G.A.: Nepreryvnoe obrazovanie v kontekste modernizatsii. [Continuing education in the context of modernization]. Moscow: IS RAN, FGNU TsSI, p. 232 (2011)
Muthyala, R., Wood, S., Jin, Y., Qin, Y., Gao, H., Rai, A.: Data-driven job search engine using skills and company attribute filters. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (2017)
Karakatsanis, I., et al.: Data mining approach to monitoring the requirements of the job market: a case study. Inf. Syst. 65, 16 (2017)
Mller, O., Schmiedel, T., Gorbacheva, E., Brocke, J.V.: Towards a typology of business process management professionals: identifying patterns of competences through latent semantic analysis. Enterp. Inf. Syst. 10, 5080 (2014)
Zhao, M., Javed, F., Jacob, F., McNair, M.: SKILL: a system for skill identification and normalization. In: Proceedings of the Twenty-Seventh Conference on Innovative Applications of Artificial Intelligence, pp. 4012–4018, January 2015
Sayfullina, L., Malmi, E., Kannala, J.: Learning representations for soft skill matching. In: van der Aalst, W.M.P., et al. (eds.) AIST 2018. LNCS, vol. 11179, pp. 141–152. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-11027-7_15
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on Machine Learning. Beijing, China, JMLR: W&CP 2014, vol. 32, pp. 1188–1196 (2014)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)
Szymaski, P., Kajdanowicz, T.: A scikit-based Python environment for performing multi-label classification. arXiv preprint arXiv:1702.01460 (2017)
Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 1746–1751 (2014)
Arkhipenko, K., Kozlov, I., Trofimovich, J., Skorniakov, K., Gomzin, A., Turdakov, D.: Comparison of neural network architectures for sentiment analysis of Russian tweets. In: Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference Dialogue 2016, pp. 50–58. RGGU, Moscow (2016)
Panchenko, A., Loukachevitch, N., Ustalov, D., Paperno, D., Meyer, C., Konstantinova., N.: RUSSE: the first workshop on Russian semantic similarity. In: Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference Dialogue 2015, vol. 2, pp. 89–105. RGGU, Moscow (2015)
Panchenko, A., et al.: a shared task on word sense induction for the Russian language. In: Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference Dialogue 2015, pp. 547–564. RGGU, Moscow (2018)
WordCloud for Python Documentation. https://amueller.github.io/word_cloud/. Accessed 29 Nov 2018
Acknowledgments
Research has been supported by the RFBR grant No. 18-47-860013 r_a Intelligent system for the formation of educational programs based on neural network models of natural language to meet the requirements of the digital economy. We are grateful to the students and lecturers of Chelyabinsk State University for help in preparing and marking data, as well as in conducting experiments. We are grateful to the head and IT-specialists of the Intersvyaz company (is74.ru) who provided the necessary computational platform for the experiments.
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Botov, D., Klenin, J., Melnikov, A., Dmitrin, Y., Nikolaev, I., Vinel, M. (2019). Mining Labor Market Requirements Using Distributional Semantic Models and Deep Learning. In: Abramowicz, W., Corchuelo, R. (eds) Business Information Systems. BIS 2019. Lecture Notes in Business Information Processing, vol 354. Springer, Cham. https://doi.org/10.1007/978-3-030-20482-2_15
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DOI: https://doi.org/10.1007/978-3-030-20482-2_15
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