Paper: Resume Information Extraction With Cascaded Hybrid Model

ACL ID P05-1062
Title Resume Information Extraction With Cascaded Hybrid Model
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2005
Authors
  • Kun Yu (University of Science and Technology of China, Anhui China)
  • Gang Guan (Tsinghua University, Beijing China)
  • Ming Zhou (Microsoft Research Asia, Beijing China)

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 ...