Domain Adaptation for Resume Classification Using Convolutional Neural Networks
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Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
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Date
2018
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
12
82-93
82-93
Series
Analysis of Images, Social Networks and Texts: 6th International Conference, AIST 2017, Moscow, Russia, July 27--29, 2017, Revised Selected Papers, Lecture Notes in Computer Science, Volume 10716
Abstract
We propose a novel method for classifying resume data of job applicants into 27 different job categories using convolutional neural networks. Since resume data is costly and hard to obtain due to its sensitive nature, we use domain adaptation. In particular, we train a classifier on a large number of freely available job description snippets and then use it to classify resume data. We empirically verify a reasonable classification performance of our approach despite having only a small amount of labeled resume data available.Description
Keywords
Resume classification, Convolutional neural networks, Job-market analysis
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Citation
Sayfullina, L, Malmi, E, Liao, Y & Jung, A 2018, Domain Adaptation for Resume Classification Using Convolutional Neural Networks . in W M P van der Aalst, D I Ignatov, M Khachay, S O Kuznetsov, V Lempitsky, I A Lomazova, N Loukachevitch, A Napoli, A Panchenko, P M Pardalos, A V Savchenko & S Wasserman (eds), Analysis of Images, Social Networks and Texts: 6th International Conference, AIST 2017, Moscow, Russia, July 27--29, 2017, Revised Selected Papers . Lecture Notes in Computer Science, vol. 10716, Springer, Cham, pp. 82-93, International Conference on Analysis of Images, Social Networks and Texts, Moscow, Russian Federation, 27/07/2017 . https://doi.org/10.1007/978-3-319-73013-4_8
