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Key-phrase extraction based on a combination of CRF model with document structure

  • Feng Yu*
  • , Hong Wei Xuan
  • , De Quan Zheng
  • *Corresponding author for this work
  • Harbin University of Commerce
  • Harbin University of Science and Technology
  • School of Computer Science and Technology, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Key-Phrase should not only reflect the main content of a document, but also reflect the specialty of this document. Key-Phrase extraction is an important technique in the field of text information processing. With the advent of the Internet age, on-line file shows an astonishing increase in geometry and information explosion has became the main character of this age. Searching and making use of network information becomes more difficult. Therefore, automatically extraction on keyword is required. This paper uses the idea of classification to complete the task of Key-Phrase extraction, which uses SVM to build classification model and uses CRF to extract Key-Phrases. The testing result shows that, the mentioned extraction approach has improved dramatically compared with previous methods in precision and recall rate.

Original languageEnglish
Title of host publicationProceedings of the 2012 8th International Conference on Computational Intelligence and Security, CIS 2012
Pages406-410
Number of pages5
DOIs
StatePublished - 2012
Externally publishedYes
Event2012 8th International Conference on Computational Intelligence and Security, CIS 2012 - Guangzhou, Guangdong, China
Duration: 17 Nov 201218 Nov 2012

Publication series

NameProceedings of the 2012 8th International Conference on Computational Intelligence and Security, CIS 2012

Conference

Conference2012 8th International Conference on Computational Intelligence and Security, CIS 2012
Country/TerritoryChina
CityGuangzhou, Guangdong
Period17/11/1218/11/12

Keywords

  • Feature Selection
  • Information Extraction
  • Inverse Document Frequency
  • Key-phrase
  • Term Frequency

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