A Study on the Identification and Classification of Relation Between Biotechnology Terms Using Semantic Parse Tree Kernel

시맨틱 구문 트리 커널을 이용한 생명공학 분야 전문용어간 관계 식별 및 분류 연구

  • 최성필 (한국과학기술정보연구원 정보기술연구실) ;
  • 정창후 (한국과학기술정보연구원 정보기술연구실) ;
  • 전홍우 (한국과학기술정보연구원 정보기술연구실) ;
  • 조현양 (경기대학교 문헌정보학과)
  • Received : 2011.04.13
  • Accepted : 2011.05.13
  • Published : 2011.05.30


In this paper, we propose a novel kernel called a semantic parse tree kernel that extends the parse tree kernel previously studied to extract protein-protein interactions(PPIs) and shown prominent results. Among the drawbacks of the existing parse tree kernel is that it could degenerate the overall performance of PPI extraction because the kernel function may produce lower kernel values of two sentences than the actual analogy between them due to the simple comparison mechanisms handling only the superficial aspects of the constituting words. The new kernel can compute the lexical semantic similarity as well as the syntactic analogy between two parse trees of target sentences. In order to calculate the lexical semantic similarity, it incorporates context-based word sense disambiguation producing synsets in WordNet as its outputs, which, in turn, can be transformed into more general ones. In experiments, we introduced two new parameters: tree kernel decay factors, and degrees of abstracting lexical concepts which can accelerate the optimization of PPI extraction performance in addition to the conventional SVM's regularization factor. Through these multi-strategic experiments, we confirmed the pivotal role of the newly applied parameters. Additionally, the experimental results showed that semantic parse tree kernel is superior to the conventional kernels especially in the PPI classification tasks.


Relation Extraction;Kernel-based Approaches;Parse Tree Kernels;Semantic Parse Tree Kernels;Word Sense Disambiguation


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