A Method of Recognizing Entity and Relation

  • Xinghua Fan
  • Maosong Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3651)


The entity and relation recognition, i.e. (1) assigning semantic classes to entities in a sentence, and (2) determining the relations held between entities, is an important task in areas such as information extraction. Subtasks (1) and (2) are typically carried out sequentially, but this approach is problematic: the errors made in subtask (1) are propagated to subtask (2) with an accumulative effect; and, the information available only in subtask (2) cannot be used in subtask (1). To address this problem, we propose a method that allows subtasks (1) and (2) to be associated more closely with each other. The process is performed in three stages: firstly, employing two classifiers to do subtasks (1) and (2) independently; secondly, recognizing an entity by taking all the entities and relations into account, using a model called the Entity Relation Propagation Diagram; thirdly, recognizing a relation based on the results of the preceding stage. The experiments show that the proposed method can improve the entity and relation recognition in some degree.


Event Variable Information Extraction Basic Event Question Answering Conditional Probability Distribution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xinghua Fan
    • 1
    • 2
  • Maosong Sun
    • 1
  1. 1.State Key Laboratory of Intelligent Technology and SystemsTsinghua UniversityBeijingChina
  2. 2.State Intellectual Property Office of P.R. ChinaBeijingChina

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