.png){kind=icon}
返回
Classifying online Job Advertisements through Machine Learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.future.2018.03.035.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The rapid growth of Web usage for advertising job positions provides a great opportunity for real-time labour market monitoring. This is the aim of Labour Market Intelligence (LMI), a field that is becoming increasingly relevant to EU Labour Market policies design and evaluation. The analysis of Web job vacancies, indeed, represents a competitive advantage to labour market stakeholders with respect to classical survey-based analyses, as it allows for reducing the time-to-market of the analysis by moving towards a fact-based decision making model. In this paper, we present our approach for automatically classifying million Web job vacancies on a standard taxonomy of occupations. We show how this problem has been expressed in terms of text classification via machine learning. We also show how our approach has been applied to certain real-life projects and we discuss the benefits provided to end users. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Machine learning
Text classification
Big data
NLP
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
F
IF:
6.1
论文数:
6.9K
被引数:
2.3W
机构
引用论文
分享收藏Comparison of generalist predators in winter-flooded and conventionally managed rice paddies and identification of their limiting factors.png)
SpringerPlus
OAAI
SpringerPlusIF0
OAAI分享收藏
Physiology and tonotopic organization of auditory receptors in the cricketGryllus bimaculatus DeGeer
PREAI
分享收藏分享收藏
查看更多内容