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Cluster Computing

, Volume 22, Supplement 3, pp 5195–5206 | Cite as

Named entity recognition based on conditional random fields

  • Shengli SongEmail author
  • Nan Zhang
  • Haitao Huang
Article

Abstract

Named entity recognition (NER) is one of the fundamental problems in many natural language processing applications and the study on NER has great significance. Combining words segmentation and parts of speech analysis, the paper proposes a new NER method based on conditional random fields considering the graininess of candidate entities. The recognition granularity can be divided into two levels: word-based and character-based. We use segmented text to extract characteristics according to the characteristic templates which had been trained in the training phase, and then calculate \(P(y{\vert }x)\) to get the best result from the input sequence. The paper valuates the algorithm for different graininess on large-scale corpus experimentally, and the results show that this method has high research value and feasibility.

Keywords

Named entity recognition Conditional random fields Graininess 

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Software Engineering InstituteXidian UniversityXi’anChina
  2. 2.School of Computer Science and TechnologyXidian UniversityXi’anChina

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