Challenge: Processing web texts for classifying job offers

Author

Amato, Flora ; Boselli, Roberto ; Cesarini, Mirko ; Mercorio, Fabio ; Mezzanzanica, Mario ; Moscato, Vincenzo ; Persia, Fabio ; Picariello, Antonio

Author_Institution

Dept. of Comput. Sci. & Syst., Univ. of Naples Federico II, Naples, Italy

fYear

2015

fDate

7-9 Feb. 2015

Firstpage

460

Lastpage

463

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.

Keywords

Web services; advertising; employment; labour resources; learning (artificial intelligence); pattern classification; portals; text analysis; LDA-based algorithms; Web portals; Web services; Web texts processing; explicit-rules; heterogeneous sources; job offers advertisement; job offers classification; labour market data; linear discriminant analysis; machine learning; occupations; private operators; public operators; standard classification system; Accuracy; Europe; Power capacitors; Static VAr compensators;

fLanguage

English

Publisher

ieee

Conference_Titel

Semantic Computing (ICSC), 2015 IEEE International Conference on

Conference_Location

Anaheim, CA

Type

conf

DOI

10.1109/ICOSC.2015.7050852

Filename

7050852