Domain Adaptation for Resume Classification Using Convolutional Neural Networks

Luiza Sayfullina, Eric Malmi, Yiping Liao, Alexander Jung

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)
90 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationAnalysis of Images, Social Networks and Texts: 6th International Conference, AIST 2017, Moscow, Russia, July 27--29, 2017, Revised Selected Papers
EditorsWil M.P. van der Aalst, Dmitry I. Ignatov, Michael Khachay, Sergei O. Kuznetsov, Victor Lempitsky, Irina A. Lomazova, Natalia Loukachevitch, Amedeo Napoli, Alexander Panchenko, Panos M. Pardalos, Andrey V. Savchenko, Stanley Wasserman
Place of PublicationCham
Number of pages12
ISBN (Electronic)978-3-319-73013-4
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Analysis of Images, Social Networks and Texts - Moscow , Russian Federation
Duration: 27 Jul 201729 Jul 2017

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Analysis of Images, Social Networks and Texts
Abbreviated titleAIST
Country/TerritoryRussian Federation


  • Resume classification
  • Convolutional neural networks
  • Job-market analysis


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