Chunk parsing is a basic step for the chunk-based processing.
语块识别是实现“基于语块处理方法”的基础。
Chunk parsing is an effective method to decrease the difficulty of language parsing.
组块分析是一种大大降低句法分析难度的有效手段。
This paper proposes to use Maximum Entropy (ME) model to conduct Chinese chunk parsing.
采用最大熵模型实现中文组块分析的任务。
This dissertation also discusses applying Conditional Random Fields to Chinese Chunk Parsing and our future works.
提出了未来关于应用条件随机场构建汉语词法语块分析模型的初步构想。
At first, we point out the difficulties of syntactic parsing and think that chunk parsing is one way to solve this problem.
本文首先指出当前语法分析的困难，而组块分析是一条解决问题的途径。
Then according to the idea of chunk parsing, the Chinese parsing system based on semantic analysis is designed and is implemented.
接着本文运用组块分析的思想，提出并实现了一个基于语义的汉语句法分析系统。
The experiment results show that the maximum entropy model has a very good effect on semantic chunk parsing to Chinese question sentence.
实验结果说明最大熵模型应用于汉语问句语义组块分析具有较好的效果。
In this paper, the computation of chunks not only includes chunk parsing, but also refers to the computation of similarity between chunks.
本文中对组块的计算，不仅包括组块分析，还涉及到对组块相似度计算的研究。
Based on the structure feature of the question, machine learning and chunk parsing theory, an approach for question chunk parsing using neural networks is implemented.
在中文问句的结构特点基础上，结合机器学习及组块分析理论，对问句进行组块分析，实现了基于神经网络的问句组块识别算法，并应用于银行领域自动问答系统中。
Based on the structure feature of the question, machine learning and chunk parsing theory, an approach for question chunk parsing using neural networks is implemented.
在中文问句的结构特点基础上，结合机器学习及组块分析理论，对问句进行组块分析，实现了基于神经网络的问句组块识别算法，并应用于银行领域自动问答系统中。
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