Earticle

초록

영어

Entity recognition and entity relationship extraction are two very important tasks in information extraction. Most research work in the literature treats these two work independently when processing the text. This paper proposes a novel method for performing entity recognition and entity relationship extraction simultaneously from unstructured text based on Conditional Random Fields (CRFs). This method makes use of entity features, entity relationship features and features of the triples which is composed of entities and their relationship to conduct the model training. Experiment results show that this method can recognize entity and extract entity relationship effectively.

목차

Abstract
 1. Introduction
 2. Related Work
  2.1. Entity Recognition
  2.2. Entity Relationship Extraction
 3. Simultaneously Entity and Relationship Extraction (SERE)
  3.1. The Overall Process
  3.2. Recognition Principle
  3.3. Samples of Tagging
 4. Experiment
  4.1. Data Preparation and Experiment Environment
  4.2. Performance Evaluation Metrics
  4.3. Experiment Result
  4.4. Conclusion
 Acknowledgements
 References

키워드

저자

• Jingtai Zhang [ College of Information Engineering, Shanghai Maritime University, 201306 Shanghai, China ]
• Jin Liu [ College of Information Engineering, Shanghai Maritime University, 201306 Shanghai, China ]
• Xiaofeng Wang [ College of Information Engineering, Shanghai Maritime University, 201306 Shanghai, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605