automatic word segmentation造句
例句与造句
- The chinese automatic word segmentation is an important part in the chinese information processing
汉语自动分词是中文信息处理中的重要环节。 - The present paper analyzes such problems as the validity , effect , precision , tolerance , compulsion and limit of automatic word segmentation
机器分词时会遇到分词的正确性、加工精度的可容性、机器分词的强制性、机器分词的局限性等问题。 - Nowadays , research on chinese information processing focuses on chinese automatic word segmentation , parsing , but seldom in automatic term extraction
目前，国内对中文信息处理的研究主要集中在汉语自动分词、语法分析上，对术语自动抽取的研究还不是很多。 - Besides these , the model of the chinese automatic words segmentation describedin this dissertation can be used to deal with the words segmentation in the situation of command lines
另外，本文所描述的汉语自动分词模块已可以在基于命令行的情况下，进行分词处理。 - The way to segmentation and the anticipated functional criterion that are suited to this subject are illustrated , at last the concrete design of the chinese automatic words segmentation are described , including the overall design and the design of each model
最后详细描述了汉语自动分词模块的具体设计，包括总体设计以及各模块设计等，同时给出了一些关键性的例程说明和程序设计的关键点总结。 - It's difficult to find automatic word segmentation in a sentence. 用automatic word segmentation造句挺难的
- The application of artificial neural network to solve the problem of chinese automatic word segmentation is presented . the mapping model and its performance are studied . based on a number of experiments , the performance of the model is evaluated
神经网络分词是今后分词技术发展的一个趋势，本文对分词神经网络进行了研究，建立了分词神经网络的实验系统，利用分词神经网络进行了歧义字段划分的实验。 - The automatic and accurate identification of chinese organization names is very significant to improve the accuracy of automatic word segmentation , and it will establish a good foundation for natural language comprehension , machine translation , information extraction and information retrieval
中文机构名称的自动识别对提高汉语自动分词的精确率有着重要的意义，也是自然语言理解、机器翻译、信息抽取和信息检索的基础。 - Refer to chinese automatic word segmentation based on statistics , this paper imports the mechanism of open learning , and uses the method of supervised and unsupervised learning . the word segmentation model includes credibility revising and partial tri - gram information
本文在基于统计的汉语自动分词的基础上，引入开放学习机制，通过有监督和无监督相结合的学习方法，建立包含可信度修正和部分三元语法信息的多元分词模型。 - Chinese information processing model is added to the traditional search engine , which can make search engine intelligent and personalized . chinese automatic word segmentation is the first work in chinese information processing . in this paper , a chinese word segmentation system is studied , which fits for intelligence search engine
针对歧义字段的划分问题，提出了歧义字段划分的三个原则，在三原则的基础上给出了“二字续分法”分词的方案，该方案能够快速有效的分解大部分的歧义字段，具有很高的实用价值。 - Chinese automatic word segmentation is the fundamental task of the chinese information processing . it mainly comprises of three difficult questions , including word criterion , disambiguation , unknown word identifying . many researchers have contributed to this field , but in the present days , it still needs pursuing higher precision
汉语自动分词是中文信息处理领域的基础课题,而且也是进行其它中文信息处理的前提,它有三个主要难点分别是分词规范,歧义字段切分和未登录词,国内外许多研究人员在这一领域都进行了深入的研究,但就目前现状来看,分词的正确率仍然有提升的空间。 - At first system accomplishes chinese language automatic word segmentation and part - of - speech tagging through chinese input approach with word segmentation , then forms corresponding surface semantic network according to the semantic structure grammar , and finally gets corresponding data flow diagram and data dictionary according to the automatic generation algorithms of data flow diagram and data dictionary , the whole completion of the work , can not only provide a description environment of natural language for case , but also develop into the system which takes the question described on the basis of the natural language as the system ' s input
工作的中心是自然语言篇章理解。系统首先通过分词输入法实现汉语自动分词与词性标注，然后根据语义结构文法产生相应的表层语义网络，最后根据数据流图、数据字典自动生成算法转换为相应的数据流图和数据字典。这项工作的彻底完成，不仅可以给case提供一个自然语言的描述环境，而且可进一步发展为基于自然语言描述问题作为输入的系统。 - Through discussing such core technologies in the automatic processing of chinese information as automatic word segmentation , feature selecting and automatic representation of texts , the thesis makes some improvements and perfection on the current methods of automatic word segmentation and text space reduction of chinese texts , therefore improved their efficiencies and effects . with regard to the methods of text classification , the paper introduced two supervisory automatic classification methods of chinese texts based on multi - classification , i . e . fuzzy clustering and boosting , which settled the problem of low percentage of recall . through comparing the results of experiments with the two methods , an automatic classification system of multi - classification texts is constructed based on the boosting method , which received good effects in application and provides a good resolution to the problem of real - time classification of information
通过对汉语信息自动处理中自动分词、特征提取、文本自动表示等核心技术讨论，对目前汉语文本自动分词和文本降维方法中的不足和缺陷作了改进，提高了分词和文本分类的效率和效果；在文本自动分类方法上，介绍了两种有监督的基于多类的汉语文本自动分类处理方法? ?模糊聚类方法和boosting方法，解决了实践中文本分类查全率不高的问题；通过对两种方法的实验比较结果，构建了基于boosting方法的多类文本自动分类系统，在实际应用中收到了良好的效果，较好的解决了信息的实时分类问题。 - It discusses and analyzes about automatic word segmentation methods for the chinese language emphatically . moreover it gives the application expectation among this system a method of word segmentation based on the reverse directional maximum matching method . it also proposes that the key of the word segmentation still needs an intact and rational word segmentation dictionary
本文首先研究和讨论了基于自然语言的语义分析方法，对汉语的自动分词方法进行了着重讨论和分析，并给出了一种基于反向最大匹配法的分词方法在本系统中的应用展望，提出分词的关键还需要一个完整合理的分词词典。 - The ictclas ( chinese accidence analysis system ) developped by institute of computation technology of china academy of science is used to implement chinese automatic word segmentation and tagging . the algorithm of data mining is used to extract meaningful features that represent the main characteristics of the retrieved documents . then , a novel ideal of virtual concept is proposed to organize the extracted features into specific concepts
用中科院计算所的ictclas汉语词法分析系统实现中文分词及标引，并用数据挖掘算法提取文档特征词条，然后利用本文提出的虚拟概念的思想，将所有特征词条组织成更有意义的概念，最后，根据概念间的继承关系，建立领域自适应概念层次结构，实现了本体论的自动构造。