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Home > english-chinese > "extract content" in Chinese

Chinese translation for "extract content"

浸出物量
萃取物景;萃取率


Related Translations:
extracts:  浸膏剂
dyewood extract:  木本植物染料萃取物染料木萃
pimento extract:  甘椒提取物 其主要成分是从甘椒提取是丁子香酚和麝香草酚。
extracting tank:  提取罐
vanilla extract:  捍子兰浸液香草精
extracted tooth:  拔掉的牙
bilberry extracts:  山桑子
yuanhu extract:  元胡提取物
extracted digit:  抽出的数字抽出数位
puerariae extract:  葛根提取物
Example Sentences:
1.Tea - determination of water extracts content
茶水浸出物测定
2.Determination of water extracts content
水浸出物测定
3.Enterprise client : this is used to capture , analyse , report and extract content
企业客户:它用于捕获、分析、报告并摘录内容。
4.In the thesis , it investigates the methods to extract content from web pages and the technologies to process content information based on the mobile platform
本文研究了面向移动平台的网络信息获取和加工技术。主要目标就是如何在移动平台上更好地利用现有的互联网信息资源。
5.The traditional methods on extracting content are representing the web page with the data structure of trees in the main memory . unfortunately , the space and time complexity is relatively high when building and searching the tree
传统的方法是将网页结构表示成树,但建树与查找树的过程中,空间与时间复杂度都过高。
6.However , this technology still faces much difficulty to bridge the semantic gap between image semantic features and the lower features , resulting in the fact that the extracted content features still mainly centered upon the lower features such as color , texture and shape
然而,由于图像语义特征和低层特征的“鸿沟”问题,给图像检索技术带来了很大困难,目前提取的内容特征仍集中于颜色、纹理、形状等低层特征。
7.We implemented an approach conjoining formula and statistic for extracting content text from chinese news web page . this method not only overcomes the shortcomings of the wrapper method but also has a high precision . besides , our method is easy to be implemented and maintained
本文根据中文网页的特性,实现了一种利用结构树从新闻类网页中提取正文信息的方法,该方法在克服了包装器方法的缺点的同时还保证了较高的准确率,具有实现简单、易于维护的特点。
8.The thesis analyses and parses openoffice . org document from text classification viewpoint , depicts the methods of extracting content , formatting , structure and descriptive information , which are most related to classification , from openoffice . org documents , and then constructs three different classification models for openoffice . org documents , respectively called label components classifier , structure components classifier and comprehensive classifier . the thesis implements these three classifiers through na ? ve bayes
本文从面向分类的角度深入地分析了openoffice . org文档,描述了从文档中抽取与分类最相关的内容和格式、结构以及文档描述信息的方法,构建了标签组件法、结构组件法和综合法三种不同的文本分类模型,最后用朴素贝叶斯方法实现了openoffcice . org文档的三种分类器。
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