Exploiting the role of position feature in Chinese relation extraction

Peng Zhang, Wenjie Li, Furu Wei, Qin Lu, Yuexian Hou

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

9 Citations (Scopus)


Relation extraction is the task of finding pre-defined semantic relations between two entities or entity mentions from text. Many methods, such as feature-based and kernel-based methods, have been proposed in the literature. Among them, feature-based methods draw much attention from researchers. However, to the best of our knowledge, existing feature-based methods did not explicitly incorporate the position feature and no in-depth analysis was conducted in this regard. In this paper, we define and exploit nine types of position information between two named entity mentions and then use it along with other features in a multi-class classification framework for Chinese relation extraction. Experiments on the ACE 2005 data set show that the position feature is more effective than the other recognized features like entity type/subtype and character-based N-gram context. Most important, it can be easily captured and does not require as much effort as applying deep natural language processing.
Original languageEnglish
Title of host publicationProceedings of the 6th International Conference on Language Resources and Evaluation, LREC 2008
PublisherEuropean Language Resources Association (ELRA)
Number of pages5
ISBN (Electronic)2951740840, 9782951740846
Publication statusPublished - 1 Jan 2008
Event6th International Conference on Language Resources and Evaluation, LREC 2008 - Palais des Congres Mansour Eddahbi, Marrakech, Morocco
Duration: 28 May 200830 May 2008


Conference6th International Conference on Language Resources and Evaluation, LREC 2008

ASJC Scopus subject areas

  • Library and Information Sciences
  • Linguistics and Language
  • Language and Linguistics
  • Education

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