Learning representations for soft skill matching

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A4 Artikkeli konferenssijulkaisussa
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Date
2018-01-01
Department
Department of Computer Science
School services,SCI
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Language
en
Pages
12
141-152
Series
Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 11179 LNCS
Abstract
Employers actively look for talents having not only specific hard skills but also various soft skills. To analyze the soft skill demands on the job market, it is important to be able to detect soft skill phrases from job advertisements automatically. However, a naive matching of soft skill phrases can lead to false positive matches when a soft skill phrase, such as friendly, is used to describe a company, a team, or another entity, rather than a desired candidate. In this paper, we propose a phrase-matching-based approach which differentiates between soft skill phrases referring to a candidate vs. something else. The disambiguation is formulated as a binary text classification problem where the prediction is made for the potential soft skill based on the context where it occurs. To inform the model about the soft skill for which the prediction is made, we develop several approaches, including soft skill masking and soft skill tagging. We compare several neural network based approaches, including CNN, LSTM and Hierarchical Attention Model. The proposed tagging-based input representation using LSTM achieved the highest recall of 83.92% on the job dataset when fixing a precision to 95%.
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DOI
10.1007/978-3-030-11027-7_15
Citation
Sayfullina, L, Malmi, E & Kannala, J 2018, Learning representations for soft skill matching . in A Panchenko, W M van der Aalst, M Khachay, P M Pardalos, V Batagelj, N Loukachevitch, G Glavaš, D I Ignatov, S O Kuznetsov, O Koltsova, I A Lomazova, A V Savchenko, A Napoli & M Pelillo (eds), Analysis of Images, Social Networks and Texts - 7th International Conference, AIST 2018, Revised Selected Papers . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11179 LNCS, Springer, pp. 141-152, International Conference on Analysis of Images, Social Networks and Texts, Moscow, Russian Federation, 05/07/2018 . https://doi.org/10.1007/978-3-030-11027-7_15
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https://urn.fi/URN:NBN:fi:aalto-202001021388
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[article-cris] Perustieteiden korkeakoulu / SCI