Kernel-based learning for biomedical relation extraction

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

56 Scopus Citations
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Author(s)

  • Jiexun Li
  • Zhu Zhang
  • Xin Li
  • Hsinchun Chen

Detail(s)

Original languageEnglish
Pages (from-to)756-769
Journal / PublicationJournal of the American Society for Information Science and Technology
Volume59
Issue number5
Online published8 Feb 2008
Publication statusPublished - Mar 2008
Externally publishedYes

Abstract

Relation extraction is the process of scanning text for relationships between named entities. Recently, significant studies have focused on automatically extracting relations from biomedical corpora. Most existing biomedical relation extractors require manual creation of biomedical lexicons or parsing templates based on domain knowledge. In this study, we propose to use kernel-based learning methods to automatically extract biomedical relations from literature text. We develop a framework of kernel-based learning for biomedical relation extraction. In particular, we modified the standard tree kernel function by incorporating a trace kernel to capture richer contextual information. In our experiments on a biomedical corpus, we compare different kernel functions for biomedical relation detection and classification. The experimental results show that a tree kernel outperforms word and sequence kernels for relation detection, our trace-tree kernel outperforms the standard tree kernel, and a composite kernel outperforms individual kernels for relation extraction.