挑战:处理网络文本来分类工作机会

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) Pub Date : 2015-03-02 DOI:10.1109/ICOSC.2015.7050852

F. Amato, R. Boselli, M. Cesarini, Fabio Mercorio, Mario Mezzanzanica, V. Moscato, Fabio Persia, A. Picariello

{"title":"挑战:处理网络文本来分类工作机会","authors":"F. Amato, R. Boselli, M. Cesarini, Fabio Mercorio, Mario Mezzanzanica, V. Moscato, Fabio Persia, A. Picariello","doi":"10.1109/ICOSC.2015.7050852","DOIUrl":null,"url":null,"abstract":"Today the Web represents a rich source of labour market data for both public and private operators, as a growing number of job offers are advertised through Web portals and services. In this paper we apply and compare several techniques, namely explicit-rules, machine learning, and LDA-based algorithms to classify a real dataset of Web job offers collected from 12 heterogeneous sources against a standard classification system of occupations.","PeriodicalId":126701,"journal":{"name":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"40","resultStr":"{\"title\":\"Challenge: Processing web texts for classifying job offers\",\"authors\":\"F. Amato, R. Boselli, M. Cesarini, Fabio Mercorio, Mario Mezzanzanica, V. Moscato, Fabio Persia, A. Picariello\",\"doi\":\"10.1109/ICOSC.2015.7050852\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Today the Web represents a rich source of labour market data for both public and private operators, as a growing number of job offers are advertised through Web portals and services. In this paper we apply and compare several techniques, namely explicit-rules, machine learning, and LDA-based algorithms to classify a real dataset of Web job offers collected from 12 heterogeneous sources against a standard classification system of occupations.\",\"PeriodicalId\":126701,\"journal\":{\"name\":\"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-03-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"40\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOSC.2015.7050852\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOSC.2015.7050852","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 40

摘要

今天，随着越来越多的工作机会通过门户网站和服务发布广告，网络为公共和私营运营商提供了丰富的劳动力市场数据来源。在本文中，我们应用并比较了几种技术，即显式规则、机器学习和基于lda的算法，将从12个异构来源收集的Web工作机会的真实数据集与标准的职业分类系统进行分类。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Challenge: Processing web texts for classifying job offers

Today the Web represents a rich source of labour market data for both public and private operators, as a growing number of job offers are advertised through Web portals and services. In this paper we apply and compare several techniques, namely explicit-rules, machine learning, and LDA-based algorithms to classify a real dataset of Web job offers collected from 12 heterogeneous sources against a standard classification system of occupations.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015)

自引率

0.00%

发文量