@inproceedings{fcdec6c554b146dd81cb720b10c010e7,
title = "Domain Adaptation for Resume Classification Using Convolutional Neural Networks",
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.",
keywords = "Resume classification, Convolutional neural networks, Job-market analysis",
author = "Luiza Sayfullina and Eric Malmi and Yiping Liao and Alexander Jung",
year = "2018",
doi = "10.1007/978-3-319-73013-4_8",
language = "English",
isbn = "978-3-319-73012-7",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "82--93",
editor = "{van der Aalst}, {Wil M.P.} and Ignatov, {Dmitry I.} and Michael Khachay and Kuznetsov, {Sergei O.} and Victor Lempitsky and Lomazova, {Irina A.} and Natalia Loukachevitch and Amedeo Napoli and Alexander Panchenko and Pardalos, {Panos M.} and Savchenko, {Andrey V.} and Stanley Wasserman",
booktitle = "Analysis of Images, Social Networks and Texts: 6th International Conference, AIST 2017, Moscow, Russia, July 27--29, 2017, Revised Selected Papers",
address = "Germany",
note = "International Conference on Analysis of Images, Social Networks and Texts , AIST ; Conference date: 27-07-2017 Through 29-07-2017",
}