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extract content in Chinese

Pronunciation:
How to pronounce "extract content""extract content" in a sentence

Translationmobile phoneMobile

  • 浸出物量
  • 萃取物景；萃取率

Examples

  • Tea - determination of water extracts content
    茶水浸出物測定
  • Determination of water extracts content
    水浸出物測定
  • Enterprise client : this is used to capture , analyse , report and extract content
    企業(yè)客戶：它用于捕獲、分析、報告并摘錄內容。
  • 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
    本文研究了面向移動平臺的網(wǎng)絡信息獲取和加工技術。主要目標就是如何在移動平臺上更好地利用現(xiàn)有的互聯(lián)網(wǎng)信息資源。
  • 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
    傳統(tǒng)的方法是將網(wǎng)頁結構表示成樹,但建樹與查找樹的過程中,空間與時間復雜度都過高。
  • 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,由于圖像語義特征和低層特征的“鴻溝”問題,給圖像檢索技術帶來了很大困難,目前提取的內容特征仍集中于顏色、紋理、形狀等低層特征。
  • 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
    本文根據(jù)中文網(wǎng)頁的特性,實現(xiàn)了一種利用結構樹從新聞類網(wǎng)頁中提取正文信息的方法,該方法在克服了包裝器方法的缺點的同時還保證了較高的準確率,具有實現(xiàn)簡單、易于維護的特點。
  • 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
    本文從面向分類的角度深入地分析了openoffice . org文檔,描述了從文檔中抽取與分類最相關的內容和格式、結構以及文檔描述信息的方法,構建了標簽組件法、結構組件法和綜合法三種不同的文本分類模型,最后用樸素貝葉斯方法實現(xiàn)了openoffcice . org文檔的三種分類器。

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