| 摘要: |
| 提出了基于复杂特征的VN结构模板获取模型.首先用统计决策树模型生长动词分类树,然后用最小描述长度原则对动词分类树剪枝,最后由动词分类树推导出VN结构模板.实验证明,在利用结构模板进行VN结构的识别时,这种模型比基于义类和极大似然估计原则的模型具有更高的精确率和召回率. |
| 关键词: 自然语言处理,语料库,复杂特征集,统计决策树,最小描述长度原则. |
| DOI： |
| 分类号: |
| 基金项目:本文研究得到国家自然科学基金资助. |
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| The Complex-feature-based Model for Acquisition of VN-construction Structure Templates |
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ZHAO Jun,HUANG Chang-ning
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| Abstract: |
| In this paper, a complex-feature- and MDL-based model for acquisition of VN-construction structure templates is put forward. First, a verb classification tree is created using statistical decision tree model. Then, the tree is pruned based on MDL (minimum description length) principle. Finally, structure templates are derived based on the verb classification tree. The experiments show that using the structure templates acquired with the model to recognizing VN-structure, the system has its advantages over the model based on the sense and the MLE (maximum likelihood estimation) principle in precision and recall. |
| Key words: Natural language processing, corpus, complex feature, statistical decision tree, minimum description length principle. |
