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An Algorithm that Learns What‘s in a Name
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Automatic segmentation of text into structured records
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Text categorization for multi-page documents: a hybrid naive Bayes HMM approach
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Bayesian information extraction network
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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ACM Transactions on Knowledge Discovery from Data (TKDD)
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
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HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
An approach to extract special skills to improve the performance of resume selection
DNIS'10 Proceedings of the 6th international conference on Databases in Networked Information Systems
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ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
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EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
An ontology-based information extraction approach for résumés
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iHR: an online recruiting system for Xiamen Talent Service Center
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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This paper presents an effective approach for resume information extraction to support automatic resume management and routing. A cascaded information extraction (IE) framework is designed. In the first pass, a resume is segmented into a consecutive blocks attached with labels indicating the information types. Then in the second pass, the detailed information, such as Name and Address, are identified in certain blocks (e.g. blocks labelled with Personal Information), instead of searching globally in the entire resume. The most appropriate model is selected through experiments for each IE task in different passes. The experimental results show that this cascaded hybrid model achieves better F-score than flat models that do not apply the hierarchical structure of resumes. It also shows that applying different IE models in different passes according to the contextual structure is effective.