论文类型：期刊论文
发表刊物：CHINESE JOURNAL OF ELECTRONICS
收录刊物：EI、SCIE、Scopus
卷号：20
期号：3
页面范围：476-482
ISSN号：1022-4653
关键字：Composite kernel; Structed features; Flat features; Hedges scope detection
摘要：To distinguish factual and uncertain information in biological texts, hedged information detection has received considerable interest in the biomedical natural language processing, which remains a challenging task due to the complexity of the syntactic and semantic analysis. This paper presents an approach to hedges scope detection using a composite kernel which combines structured and flat features. The composite kernel consists of two individual kernels: a polynomial kernel that exploits the flat features widely used in hedges scope detection and a tree kernel that captures the syntactic structured features. Four structured features over a parse tree are explored for hedges scope learning to investigate the effect of the structured features. Experiments on the CoNLL-2010 evaluation data show that our model achieves F-scores of 87.34% on hedge identification and 57.47% on scope detection respectively, which are better than those of the previous reported systems. The analysis results show that structured syntactic features with the tree kernel is more effective for hedges scope detection than the traditional flat syntactic features without the labor of detailed features designing.