AUTOMATED PROFILE EXTRACTION AND CLASSIFICATION
Renuka S Anami, Gauri R. Rao · 2014
In this Internet era, the enterprises and companies receive thousands of resumes from the job seekers. Currently available filtering techniques and search services help the recruiters to filter thousands of resumes to few hundred potential ones. Since these filtered resumes are similar to each other, it is difficult to identify the potential resumes by examining each resume. We are investigating the issues related to the development of approaches to improve the performance of resume selection process. We have extended the notion of special features and proposed an approach to identify resumes with special skill information. In the literature, the notions of special features have been applied to improve the process of product selection in E- commerce environment. However, extending the notion of special features for the development of approach to process resumes is a complex task as resumes contain unformatted text or semi-formatted text. In this system, we have proposed an approach by considering only skills related formation of the resumes. The experimental results on the real world data-set of resumes show that the proposed approach has the potential to improve the process of resume selection. This system presents an effective approach for resume information extraction to support automatic resume management and routing. An information extraction (IE) framework is designed. The overall objective of the study is to provide the required information about the skills and experience to human resource system. This system provides the resumes to extract in a structured format for the semantic web approach. Keywords—InformationExtraction(IE), CandidateProfile, NLP,JAVA,HTML,CSS.