Verb subdivision is similar to part of speech tagging . it subdivides verbs into more detailed classes based on the result of part of speech tagging 动词细分类和词性标注有些类似,它是在词性标注基础上对其中的动词进行更细致的类别标注。
The experimental results show that the sub - classes presented by this paper can enhance the recognition system ' s performance and aid information retrieval and information extraction 实验结果表明：面向机器处理的细分类能有效地提高识别系统的性能并最终有助于信息检索和抽取。
This paper makes an intensive study of chinese word segmentation , part of speech tagging and verb subdivision of lexical analysis and develops a practical lexical analysis system named irlas 本文针对词法分析中的中文分词、词性标注和动词细分类进行了深入的研究并实现了一个实用化的词法分析系统irlas 。
In other words , it is early period experimental production group and mass production groip . the difference of the appearance is the line under the cab unit . the early period group is drawing a " s " curve 细分类上9600型有两种，这9608号有初期型的特长，司机室下面部分设计成" s "字弯曲，量产型设计成" r "弯曲。
Research on tagging of part - of - speech subcategory in modem chinese is the foundation for die research on nlp ( natural language . processing ) based on corpus , also a new project for further research 现代汉语词性细分类标注研究是当前基于语料库的自然语言处理研究的基础工作，这也是面向深层研究所遇到的新课题。
According to the speciality of verb subdivision , this paper introduces a method of improved hmm to subdivide verbs . by comparing with the method of maximum entropy , it proves that this method is very effective 根据动词细分类自身的特点,本文提出了一种改进的隐马尔科夫模型的方法进行动词类别的自动划分,通过与最大熵的方法进行比较,证明这种方法十分有效。
Part of speech tagging and verb subdivision can provide richer grammatical information for upper level application . for example , parser can utilize the information of part of speech to distingulish the syntactical relationships of different types 词性标注和动词细分类可以为上层应用提供更丰富的语法信息,例如句法分析可以利用这些词性信息进行句法关系的识别。
We adopt two different characteristic extracting methods and recognition algorithms accordingly . thirdly , we researched the neutral network algorithm and its improvement algorithm , designed a bp neutral network , which could apply in chinese handwritten recognition 汉字识别的分类算法包括对汉字进行粗分类和细分类，在不同的分类方法中各采用两种互补的特征抽取算法，并相应地在识别上采用不同的策略。
The model made rough classification to the human ears first according to their geometric features , then ica was used to extract the algebra features and support vector machine ( svm ) was for detailed classification , finally the results were achieved , which was in accordance with human natural recognition process 该模型首先根据人耳的几何特征对人耳进行粗分类；然后应用独立分量分析的方法提取代数特征，支持向量机进行细分类，最后给出分类结果。
There are three steps in the combined . firstly , the template matching method is used to classify roughly in the first step ; secondly , the fuzzy pattern recognition method which are connected paralled is used to classify finely ; at last , the third classifier , which input is the output of the second classifiers , raise one ’ s hand as its decision rules 第一级分类器采用模板匹配法进行粗分类;第二级分类器由并行的模糊模式识别分类器组成,进行细分类;第三级分类器是综合分类器,它将第二级的输出作为输入,根据举手表决规则得到最后的输出结果。
