Learning and Inference for Clause Identification
Learning and Inference for Clause Identification
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子句识别的学习和推理
DOI：
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发表时间：
2002
期刊：
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通讯作者：
D. Roth
中科院分区：
文献类型：
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作者：
X. Carreras;Lluís Màrquez i Villodre;Vasin Punyakanok;D. Roth
本文提出了一种自然语言句子的部分解析方法，该方法在分层学习的局部分类器的结果之上进行全局推理。一个句子到子句的最佳分解是使用一个基于动态规划的计划，考虑到以前确定的部分解决方案。这个推理方案在几个层次上应用学习--当识别潜在的子句时和当对部分解决方案进行评分时。分类器以分层的方式进行训练，建立在以前的分类基础上。提出的方法显着优于最好的方法，迄今为止已知的子句识别。
This paper presents an approach to partial parsing of natural language sentences that makes global inference on top of the outcome of hierarchically learned local classifiers. The best decomposition of a sentence into clauses is chosen using a dynamic programming based scheme that takes into account previously identified partial solutions. This inference scheme applies learning at several levels--when identifying potential clauses and when scoring partial solutions. The classifiers are trained in a hierarchical fashion, building on previous classifications. The method presented significantly outperforms the best methods known so far for clause identification.