Resume Information Extraction with Cascaded Hybrid Model

Kun Yu,Gang Guan,M. Zhou

Published 2005 in Annual Meeting of the Association for Computational Linguistics

ABSTRACT

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.

PUBLICATION RECORD

  • Publication year

    2005

  • Venue

    Annual Meeting of the Association for Computational Linguistics

  • Publication date

    2005-06-25

  • Fields of study

    Computer Science

  • Identifiers
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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