本文首先讨论语义角色的三种精细程度不一的分类层级，介绍它们各自在语言信息处理系统中的有关应用。
This paper firstly discusses three kinds of level in the fineness hierarchy of semantic roles, and introduces their application in some systems of natural language processing(NPL).
这一方法将汉语语义角色标注从一个节点的分类问题转化为序列标注问题，我们使用了条件随机域这一模型，取得了较好的结果。
Based on the semantic chunking result, the Chinese SRL can be changed into a sequence labeling problem instead of the classification problem.
本文还讨论了汉语动词的分类及动词支配的语义成分，对动元的语义角色逐一作了说明。
The paper also discusses classification of verbs and semantic elements disposed by verbs and de- fines semantic role of germ- verbs one by one.
在第一阶段先进行二元分类，判别一个句法成分是否为语义角色，然后对第一阶段中的语义角色再进行多元分类，给其分配具体的语义角色。
In the first phase, we carried out the dual classification, determine whether a syntactic component is a semantic role, then do multi-classification to assign a specific semantic role.
在第一阶段先进行二元分类，判别一个句法成分是否为语义角色，然后对第一阶段中的语义角色再进行多元分类，给其分配具体的语义角色。
In the first phase, we carried out the dual classification, determine whether a syntactic component is a semantic role, then do multi-classification to assign a specific semantic role.
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