@article{zhou2008hierarchical,
  abstract = {This paper proposes a novel tree kernel-based method with rich syntactic and semantic information for the extraction of semantic relations between named entities. With a parse tree and an entity pair, we first construct a rich semantic relation tree structure to integrate both syntactic and semantic information. And then we propose a context-sensitive convolution tree kernel, which enumerates both context-free and context-sensitive sub-trees by considering the paths of their ancestor nodes as their contexts to capture structural information in the tree structure. An evaluation on the Automatic Content Extraction/Relation Detection and Characterization (ACE RDC) corpora shows that the proposed tree kernelbased method outperforms other state-of-the-art methods.},
  address = {Tarrytown, NY, USA},
  author = {Zhou, GuoDong and Zhang, Min and Ji, DongHong and Zhu, QiaoMing},
  doi = {http://dx.doi.org/10.1016/j.ipm.2007.07.007},
  file = {zhou2008hierarchical.pdf:zhou2008hierarchical.pdf:PDF},
  groups = {public},
  interhash = {e5e2d51cf1f3a6d5efc3bd25c40602c8},
  intrahash = {b7eb173bc2c3dd1311a24ae9a96e5c2c},
  issn = {0306-4573},
  journal = {Information Process Managegement},
  journalpub = {1},
  number = 3,
  pages = {1008--1021},
  publisher = {Pergamon Press, Inc.},
  timestamp = {2010-06-10 10:51:05},
  title = {Hierarchical learning strategy in semantic relation extraction},
  url = {http://nlp.suda.edu.cn/~gdzhou/publication/zhougd2010_INS_ContextSensitiveTreeKernelforRelationExtraction.pdf},
  username = {dbenz},
  volume = 44,
  year = 2008
}