广西师范大学学报（自然科学版）2011，Vol.29Issue(3)：147-150,4.
基于CRF和转换错误驱动学习的浅层句法分析
Shallow Parsing Based on CRF and Transformation-based Error-driven Learning
摘要
Abstract
This paper proposes a method for shallow parsing on the basis of CRF and transformation-based error-driven learning. The method is applied to Penn Chinese Treebank and gets a good performance of chunking identification. First,CRF model is used to identify chunks to acquire candidate transformation rules by error-driven learning. Then,an evaluation function is used to filter candidate transformation rules. And last,transformation rules are used to revise the chunking results of CRF. The experimental results show that this approach is effective, and outperforms the single CRF-based approach in shallow parsing. Precision,recall and F-values are improved respectively.关键词
浅层句法分析/CRF/转换错误驱动学习/转换规则集Key words
shallow parsing/CRF/transformation-based error-driven learning/transformation rules分类
信息技术与安全科学引用本文复制引用
张芬,曲维光,赵红艳,周俊生..基于CRF和转换错误驱动学习的浅层句法分析[J].广西师范大学学报（自然科学版）,2011,29(3):147-150,4.基金项目
国家自然科学基金资助项目(60773173,61073119) （60773173,61073119）
国家哲学社科基金资助项目(10CYY021) （10CYY021）
江苏省自然科学基金资助项目(BK2010547) （BK2010547）
江苏省教育厅自然科学基金资助项目(10KJB520009) （10KJB520009）
江苏省高校社科基金资助项目(06SJB71007) （06SJB71007）
