Automatic Extraction of Hierarchical Relations from Text

  • Ting Wang
  • Yaoyong Li
  • Kalina Bontcheva
  • Hamish Cunningham
  • Ji Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4011)


Automatic extraction of semantic relationships between entity instances in an ontology is useful for attaching richer semantic metadata to documents. In this paper we propose an SVM based approach to hierarchical relation extraction, using features derived automatically from a number of GATE-based open-source language processing tools. In comparison to the previous works, we use several new features including part of speech tag, entity subtype, entity class, entity role, semantic representation of sentence and WordNet synonym set. The impact of the features on the performance is investigated, as is the impact of the relation classification hierarchy. The results show there is a trade-off among these factors for relation extraction and the features containing more information such as semantic ones can improve the performance of the ontological relation extraction task.


Support Vector Machine Support Vector Machine Model Semantic Feature Automatic Extraction Dependency Tree 
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 2006

Authors and Affiliations

  • Ting Wang
    • 1
    • 2
  • Yaoyong Li
    • 1
  • Kalina Bontcheva
    • 1
  • Hamish Cunningham
    • 1
  • Ji Wang
    • 2
  1. 1.Department of Computer ScienceUniversity of SheffieldSheffieldUK
  2. 2.Department of ComputerNational University of Defense TechnologyChangsha, HunanP.R. China

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