Domain Adaptation for Resume Classification Using Convolutional Neural Networks

Autor:	Sayfullina, Luiza, Malmi, Eric, Liao, Yiping, Jung, Alex
Rok vydání:	2017
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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. Comment: To be published in AIST proceedings: Springer's Lecture Notes in Computer Science (LNCS) series
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1707.05576 Zobrazit plný text záznamu View this record from Arxiv