Automatic Chinese segmentation is the basis of Chinese information processing.
汉语自动分词是进行中文信息处理的基础。
Chinese segmentation is a special and important issue in Chinese texts processing.
汉语分词在汉语文本处理过程中是一个十分特殊而重要的组成部分。
A word that is not included in a Chinese segmentation lexicon is called a new word.
新词指在进行词法切分时词典中未收录的词。
As a basic component of Chinese word segmentation system, the dictionary mechanism influences the speed and the efficiency of segmentation significantly.
词典是中文自动分词的基础，分词词典机制的优劣直接影响到中文分词的速度和效率。
The primary segmentation point is obtained by analysis of whole Chinese word image connection area.
对整个汉字图像进行连通域分析，得到初选的分割点。
This paper presented a syllable segmentation method for Chinese connected speech.
本文提出一种汉语语音连接词音节分割方法。
In this paper, we first construct the system architecture, improve the Chinese text segmentation algorithm, then, by making use of domain ontology base and sentence similarity, design the system.
文章首先构造了自动答疑系统架构，改进了中文分词算法，并利用领域本体库和语句相似度设计了该系统。
Aiming at the dissatisfied effect of Chinese word segmentation to Email texts, an improved Maximum Match Based Approach is presented.
针对邮件文本分词效果较差的特点，提出采用一种改进的最大匹配法来进行中文分词的方法。
Chinese word segmentation is always the first step of subject extraction. The quality of word segmentation is effective to the quality of text subject extraction.
而主题提取是以中文分词作为第一步，分词质量直接影响到文献主题提取的质量。
Especially, it is very difficult to deal with special noun in Chinese automatic word segmentation.
特别是对专有名词的处理是中文自动分词中的又一个难点。
The method mentioned above has been applied to the segmentation of Chinese bank check amounts and get good results.
上述方法应用于银行支票手写体大写金额的分割，取得了很好的分割效果。
Here we explore SVM for a Chinese word segmentation task, use the context attributes and rule-based attributes as the features for a sample.
本文首次使用SVM方法来完成中文分词的任务，使用上下文窗体属性和基于规则的属性对样本进行刻画。
In order to solve the problem of automatic segmentation of handwritten Chinese text, a dynamic programming-based online handwritten Chinese character segmentation method is proposed.
为解决手写汉字文本的自动切分问题，提出了一种基于动态规划的联机手写汉字分割方法。
Chinese automatic segmentation is one of the most difficult problems in computer Chinese information disposal and the key problem that document content analysis must resolve.
汉语自动分词是计算机中文信息处理中的难题，也是文献内容分析中必须解决的关键问题之一。
This paper proposes a statistical method to solve overlapped ambiguity in Chinese words' segmentation.
该文利用一种统计的方法来解决交集型歧义字段的切分。
This paper presents a learning method to auto ma tically acquire segmentation knowledge from Chinese corpus.
文章描述了一种从熟语料中自动获取文本切分知识的机器学习的方法。
In this paper, the dictionary mechanism is dynamic TRIE tree, and we have designed the Chinese word segmentation dictionary. The dictionary USES less memory.
论文采用动态TRIE索引树的词典机制，设计并实现了汉语分词词典，有效地减少了词典空间。
Knowledge of Chinese words automatic segmentation can raise the precision of automatic segmentation, and it can satisfy high precision requirements.
使用自动分词知识可以进一步提高自动切分精度，满足高标准的需求。
Combinational ambiguity is a challenging issue in Chinese word segmentation in that its disambiguation depends on the contextual information.
组合型歧义切分字段一直是汉语自动分词的难点，难点在于消歧依赖其上下文语境信息。
Automatic Chinese word segmentation is the basis of Chinese information processing.
汉语自动分词是进行中文信息处理的基础。
A fast algorithm for generating Chinese word segmentation digraph was given.
给出了一种汉语分词有向图的快速生成算法。
This paper puts forward a new algorithm about automatic Chinese text segmentation based on Chinese characters string frequency and length descending.
提出了一种基于汉字串频度及串长度递减的中文文本自动切分算法。
To extend word segmentation repository and enhance word segmentation capacity, a Chinese word segmentation system based on automatic learning is proposed in this paper.
为扩展分词知识库，提高自动分词能力，本文提出了一种基于自学习机制的汉语自动分词系统。
Overlapping ambiguity is a major type of ambiguity in Chinese word segmentation.
交集型分词歧义是汉语自动分词中的主要歧义类型之一。
The Chinese words segmentation and labeling are basis of the Chinese language processing.
汉语的分词及词性标注是汉语语言处理的基础。
The former includes Chinese word segmentation, part - of - speech tagging, pinyin tagging, named entity recognition, new word detection, syntactic parsing, word sense disambiguation, etc.
前者涉及到词法、句法、语义分析，包括汉语分词、词性标注、注音、命名实体识别、新词发现、句法分析、词义消歧等。
The former includes Chinese word segmentation, part - of - speech tagging, pinyin tagging, named entity recognition, new word detection, syntactic parsing, word sense disambiguation, etc.
前者涉及到词法、句法、语义分析，包括汉语分词、词性标注、注音、命名实体识别、新词发现、句法分析、词义消歧等。
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