Automatic Profiling System for Ranking Candidates Answers in Human Resources

The exponential growth of Internet allowed the development of a market of online job search sites. This work aims at presenting the E-Gen system (Automatic Job Offer Processing system for Human Resources). E-Gen will implement several complex tasks: an analysis and categorization of jobs offers which are unstructured text documents (e-mails of job offers possibly with an attached document), an analysis and a relevance ranking of the candidate answers. We present a strategy to resolve the last task: After a process of filtering and lemmatisation, we use vectorial representation and different similarity measures. The quality of ranking obtained is evaluated using ROC curves.

[1]  Toshio Odanaka,et al.  ADAPTIVE CONTROL PROCESSES , 1990 .

[2]  Sanjoy Dasgupta,et al.  Adaptive Control Processes , 2010, Encyclopedia of Machine Learning and Data Mining.

[3]  Michel Leclère,et al.  The Semantic Web in e-recruitment (2004) , 2004 .

[4]  Florian Boudin,et al.  NEO-CORTEX: A Performant User-Oriented Multi-Document Summarization System , 2007, CICLing.

[5]  M. Roche,et al.  Evaluation et détermination de la pertinence pour des syntagmes candidats à la collocation , 2008 .

[6]  Barry Smyth,et al.  Passive Profiling from Server Logs in an Online Recruitment Environment , 2001, IJCAI 2001.

[7]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[8]  Juan-Manuel Torres-Moreno,et al.  E-Gen: Automatic Job Offer Processing System for Human Resources , 2007, MICAI.

[9]  Barry Smyth,et al.  Inferring Relevance Feedback from Server Logs: A Case Study in Online Recruitment , 2000 .

[10]  Robert Tolksdorf,et al.  The Impact of Semantic Web Technologies on Job Recruitment Processes , 2005, Wirtschaftsinformatik.

[11]  R. Bellman,et al.  V. Adaptive Control Processes , 1964 .

[12]  Juan-Manuel Torres-Moreno,et al.  E-Gen : Profilage automatique de candidatures , 2008 .

[13]  Eric SanJuan,et al.  Textual Energy of Associative Memories: Performant Applications of Enertex Algorithm in Text Summarization and Topic Segmentation , 2007, MICAI.

[14]  Alexander Gelbukh,et al.  MICAI 2007: Advances in Artificial Intelligence, 6th Mexican International Conference on Artificial Intelligence, Aguascalientes, Mexico, November 4-10, 2007, Proceedings , 2007, MICAI.

[15]  Lora Aroyo,et al.  The Semantic Web: Research and Applications , 2009, Lecture Notes in Computer Science.

[16]  S. B. Needleman,et al.  A general method applicable to the search for similarities in the amino acid sequence of two proteins. , 1970, Journal of molecular biology.

[17]  A. Bernstein,et al.  SimPack: A Generic Java Library for Similarity Measures in Ontologies , 2005 .

[18]  Barry Smyth,et al.  Automated Collaborative Filtering Applications for Online Recruitment Services , 2000, AH.

[19]  Hinrich Schütze,et al.  Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

[20]  Helmut Schmidt,et al.  Probabilistic part-of-speech tagging using decision trees , 1994 .

[21]  M. Leclere,et al.  Human resource management and semantic Web technologies , 2004, Proceedings. 2004 International Conference on Information and Communication Technologies: From Theory to Applications, 2004..

[22]  Michael C. Mozer,et al.  Optimizing Classifier Performance via an Approximation to the Wilcoxon-Mann-Whitney Statistic , 2003, ICML.

[23]  Peter A. Flach,et al.  Learning Decision Trees Using the Area Under the ROC Curve , 2002, ICML.

[24]  P. Bellot,et al.  Classification et segmentation de textes par arbres de décision Application à la recherche documentaire , 2001 .

[25]  G Salton,et al.  Developments in Automatic Text Retrieval , 1991, Science.