We use a simple and effective Chinese word segmentation method and compare CLIR performance.
使用了一种简单高效的汉语分词方法，比较了两种检索条件翻译方法的性能。
Index module: first of all, discuss the design method of Chinese word segmentation and choose a word segmentation algorithm.
索引模块中：首先，讨论了中文分词的设计思想，选择了分词的算法。
The method improves the accuracy of word segmentation, by combining morphology and syntax with language situation.
该方法建立在词法和句法基础上，从语境角度分析歧义字段，提高分词准确率。
Its core idea was as follows: Firstly, we employed a rule-based method to recognize some musical entities with explicit rules in their context before word segmentation.
其核心思想为：首先，在分词之前采用基于规则的方法来识别部分明显的音乐实体。
At the same time, we apply the statistics method to identify the new words that are inexistent in the dictionary and supply them to the dictionary for the later text word segmentation.
同时应用统计方法识别出词典中没有的新词，并将其补充进词典中，用于切分后续文本。
After summarizing and analyzing the state of the art on Chinese name entity extraction, we emphasize that three fundamental problems including word segmentation, domain, and method should be solved.
中文命名实体抽取的研究，存在分词、领域和方法三个方面的问题需要解决。
Maximum match method is optimized to improve the speed of the system during the word segmentation.
切分过程系统利用改进正向最大匹配算法，提高了分词切分效率。
The experimental result of 5000-words test show that the method is better accurate in Uyghur word segmentation.
在一个5000词的测试语料上进行了实验，实验结果表明，使用该方法进行维吾尔语词切分具有更高的准确率。
A Chinese word segmentation based on machine learning is presented in the paper and the machine learning model system is realized after analyzing the traditional method.
本文分析了现有分词解决方案的优势和不足，提出一种基于机器学习的中文分词方法，并实现了机器学习分词模型系统。
A Chinese word segmentation based on machine learning is presented in the paper and the machine learning model system is realized after analyzing the traditional method.
本文分析了现有分词解决方案的优势和不足，提出一种基于机器学习的中文分词方法，并实现了机器学习分词模型系统。
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