Abstract
The exponential growth of the Internet has allowed the development of a market of on-line job search sites. This paper aims at presenting the E-Gen system (Automatic Job Offer Processing system for Human Resources). E-Gen will implement two complex tasks: an analysis and categorisation of job postings, which are unstructured text documents (e-mails of job listings possibly with an attached document), an analysis and a relevance ranking of the candidate answers (cover letter and curriculum vitae). This paper aims to present a strategy to resolve the first task: after a process of filtering and lemmatisation, we use vectorial representation before generating a classification with Support Vector Machines. This first classification is afterwards transmitted to a ”corrective” post-process which improves the quality of the solution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bizer, C., Heese, R., Mochol, M., Oldakowski, R., Tolksdorf, R., Eckstein, R.: The impact of semantic web technologies on job recruitment processes. In: International Conference Wirtschaftsinformatik (WI 2005), Bamberg, Germany (2005)
Rafter, R., Bradley, K., Smyt, B.: Automated Collaborative Filtering Applications for Online Recruitment Services, 363–368 (2000)
Rafter, R., Smyth, B.: (Passive Profiling from Server Logs in an Online Recruitment Environment)
Bourse, M., Leclère, M., Morin, E., Trichet, F.: Human resource management and semantic web technologies. In: Proceedings, 1st International Conference on Information & Communication Technologies: from Theory to Applications (ICTTA) (2004)
Morin, E., Leclère, M., Trichet, F.: The semantic web in e-recruitment. In: The First European Symposium of Semantic Web (ESWS’2004) (2004)
Rafter, R., Smyth, B., Bradley, K.: (Inferring Relevance Feedback from Server Logs: A Case Study in Online Recruitment)
Zighed, D.A., J., C.: Data Mining and CV analysis 17, 189–200 (2003)
Bellman, R.: Adaptive Control Processes. Princeton University Press, Princeton (1961)
Manning, D., Schütze, H.: Foundantions of Statistical Natural Language Processing. MIT Press, Cambridge (2002)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Joachims, T.: Making large scale SVM learning practical. Advances in kernel methods: support vector learning, pp. 169–184. MIT Press, Cambridge (1999)
Grilheres, B., Brunessaux, S., Leray, P.: Combining classifiers for harmful document filtering. In: RIAO 2004, pp. 173–185 (2004)
Kessler, R., Torres-Moreno, J.M., El-Bèze, M.: Classification automatique de courriers électroniques par des méthodes mixtes d’apprentissage, 93–112 (2006)
Fan, R.-E., Chen, P.-H., Lin, C.-J.: Towards a Hybrid Abstract Generation System. In: Working set selection using the second order information for training SVM, pp. 1889–1918 (2005)
Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimal decoding algorithm 13, 260–269 (1967)
Land, A.H., Doig, A.G.: An Automatic Method of Solving Discrete Programming Problems 28, 497–520 (1960)
El-Bèze, M., Torres-Moreno, J., Béchet, F.: Un duel probabiliste pour départager deux Présidents. In: RNTI coming soon, pp. 1889–1918 (2007)
Reynar, J., Ratnaparkhi, A.: A Maximum Entropy Approach to Identifying Sentence Boundaries. In: Proceedings of the Fifth Conference on Applied Natural Language Processing, Washington, D.C., pp. 16–19 (1997)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kessler, R., Torres-Moreno, J.M., El-Bèze, M. (2007). E-Gen: Automatic Job Offer Processing System for Human Resources. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_94
Download citation
DOI: https://doi.org/10.1007/978-3-540-76631-5_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76630-8
Online ISBN: 978-3-540-76631-5
eBook Packages: Computer ScienceComputer Science (R0)