Finally, a example is used to illustrate the acquiring method of ambiguity word POS tagging rules.
最后实例说明汉语兼类词词性标注规则的获取方法。
POS tagging can bring "added value" to learner corpora and thus enable in-depth studies of interlanguage.
对学习者语料进行自动词性赋码，可以使语料库获得“增值”，便于对中介语进行更深层次的研究。
Test evaluation criteria were used in POS tagging accuracy and part-category words disambiguation accuracy.
测试评价标准分别采用了词性标注准确率和兼类词排歧准确率。
In this paper, a kind of acquisitive method for Chinese POS tagging rules based on collocation mode, is introduced.
文章介绍了一种基于搭配模式的汉语词性标注规则的获取方法。
So Conditional Random Field (CRF) is introduced to build POS tagging model in this paper, in order to overcome above problems.
论文引入条件随机域建立词性标注模型，易于融合新的特征，并能解决标注偏置的问题。
POS tagging the English texts and incorporating the features driven from the English POS results into POS tagging model for Chinese.
对英文语料库进行词性标注，以获得中文词语对应的英文单词的词性，并将其作为一个特征加入到特征模板。
This approach expands the POS tagging set based on the characteristic of answering text and the demand of key information distilling.
针对已有词类标注方法在标注网络答疑文本时存在的不足，文章提出了一种面向自然语言答疑文本的词类标注方法。
Finally, using the system to the Mongolian automatic POS tagging when Mongolian segmentation before and after is made by the following test.
最后本文对该系统对蒙古文进行切分之前和切分之后的自动词性标注分别作了以下的实验。
The model is a parser based on lexicalized model, it is combined with segmentation and POS tagging model and thus a language parser is built.
该模型是一个词汇化的句法分析模型，能结合分词、词性标注进行句法分析；
The inner structure of BaseNP can be analyzed based on different features, among them, POS tagging information is the most important feature.
确定基本名词短语内部结构的因素有多种，但基本名词短语成分的词类信息是最基本的因素。
The system includes some modules such as originally segmenting, POS tagging, ambiguity processing, model smoothing and Unknown Word Recognizing.
系统包括初切分，词性标注、歧义字段处理、模型平滑、未登录词识别等功能模块。
Experiments on Chinese TreeBank from different training set size are made. It shows that our approach improves the accuracy of POS tagging over the four training sets with different sizes.
本文以宾州中文树库为实验语料，考查了不同规模的标注数据对模型性能的影响，实验结果表明，本文提出的无监督词性标注方法提高了中文词性标注的性能。
The parser was evaluated on the standard test set with PARSEVAL metric. It performed with the precision of 85.89% and the recall rate of 85.61% on the sentences with gold segmentation and POS tagging.
在公共的测试集上对句法分析器的性能进行了评价，对于正确分词和词性标注的句子，句法分析的精确率和召回率分别达到85.89%和85.61%。
Aiming at the shortcomings occur when the existing methods tagging the web answering text, this paper proposes an approach of part of speech (pos) tagging in answering text.
针对已有词类标注方法在标注网络答疑文本时存在的不足，文章提出了一种面向自然语言答疑文本的词类标注方法。
Aiming at the shortcomings occur when the existing methods tagging the web answering text, this paper proposes an approach of part of speech (pos) tagging in answering text.
针对已有词类标注方法在标注网络答疑文本时存在的不足，文章提出了一种面向自然语言答疑文本的词类标注方法。
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