We extend previous work on tree kernels to estimate the similarity between the dependency trees of sentences. Using this kernel within a Support Vector Machine, we detect and classify relations between entities in the Automatic Content Extraction (ACE) corpus of news articles. We examine the utility of different features such as Wordnet hypernyms, parts of speech, and entity types, and find that the dependency tree kernel achieves a 20% F1 improvement over a “bag-of-words” kernel.


  author = {Aron Culotta and Jeffery Sorensen},
  title = {Dependency tree kernels for relation extraction},
  shortbooktitle = {ACL},
  booktitle = {42nd Annual Meeting of the Association for Computational Linguistics (ACL)},
  address = {Barcelona, Spain},
  year = {2004},