詞類標識 的英文怎麼說

中文拼音 [cílèibiāozhì]
詞類標識 英文
part-of-speech tagging
  • 詞 : 名詞1 （說話或詩歌、文章、戲劇中的語句） speech; statement; lines of play 2 （一種韻文形式 起於唐...
  • 類 : Ⅰ名1 （許多相似或相同的事物的綜合; 種類） class; category; kind; type 2 （姓氏） a surname Ⅱ動詞...
  • 標 : Ⅰ名詞1 [書面語] （樹梢） treetop; the tip of a tree2 （枝節或表面） symptom; outside appearance; ...
  • 識 : 識Ⅰ動詞[書面語] （記） remember; commit to memory Ⅱ名詞1. [書面語] （記號） mark; sign 2. （姓氏） a surname
  • 標識 : [物理學] characteristic; identification; identifying標識碼 identification code; 標識位置 home position
  1. And then, the paper use the measure of literal similarity to classify the chinese web pages, which based on lots of experiential classifying data. then, the author uses borland delphi and visual foxpro to develop an automatic indexing system, which is used to process chinese web pages. the experiential system is composed of web pages text analysis, automatic words extracting, automatic subject indexing, classifying, indexing result confirmation and knowledge database maintenance

    隨后，本文利用borlanddelphi 、 visualfoxpro等工具設計並開發了一個包括中文網頁文本信息提取、自動抽詞、自動主題與分類標引、標引結果處理、知識庫維護等功能，用以處理中文網頁信息的自動標引實驗系統；並簡要介紹了系統的設計、工作流程、使用方法及運行條件。
  2. The knowledge source which have relative stable form and glossary is analyzed, and the data mining step for this kind of knowledge is designed, viz. filter by key word - intercept - field specialist confirm. the standard glossary table and the " value " of each words is established, which enhance the expert system ability to knowledge acquisition and light the rough task of knowledge banks maintenance

    分析了具有相對固定格式和術語的知識源，設計了適用於這類知識源的電子文本知識挖掘的步驟，即：關鍵詞過濾?截詞?專家確認；形成了專業用語的標準化表和標準詞賦值表，增強了系統的知識獲取能力，減輕了專家系統的知識系統維護。
  3. According to the program structure of ansi c, some syntax - tree - nodes are designed in front - end, including function, block, data structure, type, expression, identifier and so on. syntax tree is divided into three levels, namely program level, function level and block level, which are useful for analysis of syntax and semantic in compiler as well as transferring syntax tree into rtl. retargetable compiler mainly includes intermediate representation, machine description and interface technique between compiler and machine description

    編譯前端的實現技術包括詞法分析、語法語義分析和中間代碼生成技術等，針對ansic的程序結構，分別設計了函數、塊、數據結構、類型、表達式、標識符等語法樹結點，並將語法樹分成編譯層次、函數聲明和塊三個不同層次，使語法樹具有清晰的層次結構，有利於編譯程序的語法和語義分析，以及從語法樹到rtl中間表示的轉換。
  4. The categories of preprocessing token are : header names, identifiers, preprocessing numbers, character literals, string literals, preprocessing - op - or - punc, and single non - white - space characters that do not lexically match the other preprocessing token categories

    預處理標記有以下幾類：頭名稱，標識符，預處理數字，字元文字量，字元串文字量，預處理運算符或標點，以及不符合以上詞法類型的單個非空白字元。
  5. The research of this paper is spread out mainly around 5 aspects : ( 1 ) the description of xmarc information theory system based on xml under network environment ; ( 2 ) the advanced design of field xmarc metadata ; ( 3 ) the foundation of k - s - c special knowledge relation by the identification " keyword + subject + category " ; ( 4 ) the establishment of xmarc theme knowledge automatic indexing and its algorithms ; ( 5 ) the research on knowledge processing method of concept retrieval and the theme classification based on xmarc

    本文主要的工作是圍繞五個方面展開的：建立網路環境下基於xml的xmarc信息描述理論體系，比較設計領域的xmarc元數據，構建以「關鍵詞+主題詞+范疇號」標識的知識關系k - s - c （ keyword - subject - category ） ，建立xmarc主題知識的標引方摘要法及其演算法，提出xmarc主題知識的分類與詞句概念檢索方法。
  6. In this paper, the word segmentation technology of chinese text classification is debated emphatically. and the method of word segmentation based on the phrase labeling of 2 - gram syntax is put forward combining the method of setting separate - signs and the method based on the statistic of word - frequency, which can recognize the vocabularies which the method based on the dictionary can not manage

    對于基於信息過濾的自動分類問題，使用字典分詞並不是一個必須的過程，因而本文提出了基於2元語法短語標引的分詞方法，它將設立切分標志法與基於詞頻統計的方法相結合，可以識別基於詞典方法處理不了的詞匯，如：人名、地名、專業術語等。
  7. As a post processing for input code of chinese characters, the issues of common interest for all kinds of input software of chinese characters is distinguish and decide coincident code of the chinese character and word by the input software, deduce average code length, impel chinese characters code to simplify and standardize

    摘要作為輸入編碼的后處理，各種類型輸入軟體智能化的共同目標是由軟體來識別和選定上屏的重碼字、詞與縮短平均碼長，並促使編碼簡單化和規范化。
  8. In further research, the following issues must be considered : 1 ) the standardize of corpus ; 2 ) improve the accuracy of chinese words divided syncopation system, handle the different meanings of one word and recognize the words that do not appear in the dictionary ; 3 ) process semantic analysis ; 4 ) dynamically update the training sets fed back by the user ; 5 ) quantitatively analyze the system performance influenced by different factors, use an appropriate model to compare and evaluate the web text classification system ; 6 ) natural language process ; 7 ) distinguish the disguise of sensitive words

    在以後的工作中考慮如下問題: 1 ）數據集的標準化; 2 ）分詞系統精度的提高，對歧義處理以及未登錄詞識別的能力的提高: 3 ）進行合理的語義分析: 4 ）利用用戶反饋信息動態更新訓練集; 5 ）定t分析分類器不同要素對分類系統性能的影響，使用合適的模型來比較和評價分類系統; 6 ）自然語言理解問題，如「引用」問題; 7 ）對于敏感詞匯偽裝的識別問題。
  9. 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

    詞性標注和動詞細分類可以為上層應用提供更豐富的語法信息，例如句法分析可以利用這些詞性信息進行句法關系的識別。
  10. Using pos tagging information as the core feature, together with other determinative conditions, we construct a rule set for mongolian basenp recognition, which will be a necessary resource for basenp recognition

    我們以詞類信息為核心，附加一些限定條件，構建識別基本名詞短語的形式規則集，並在實際語料中進行基本名詞短語標注測試。
  11. Typically, the name of a creator should be used to indicate the entity. " for computing purposes, the term " name " may have a pretty broad interpretation, and the definition above is only really effective for human consumption of the metadata

    通常creator的名稱應該用於標識該實體」 。從計算的角度看， 「名稱」一詞可能有非常寬泛的解釋，上述定義實際上只對元資料的人類消費者有效。
  12. Then user browses the web pages, selects interested web page as sample page and create conceptual schema based on the understanding of content of the sample page. thirdly, the user marks the interested information blocks in sample page and build up the correspondence between the interested information blocks of sample page and fields of conceptual schema, and at the meanwhile system passes the correspondence to learning module to form extraction rules and stores them into rule database. fourthly, extraction module extracts information from the similar - structured pages by using the extraction ailes and sends the extracted results to classified cache database for users " further query

    該系統首先將web信息按模式和風格劃分為不同領域，建立領域知識庫，以便對用戶的查詢進行導航並縮小查詢范圍；然後利用現有的搜索引擎實現關鍵詞查詢，利用瀏覽功能尋找感興趣的網頁，對找到的網頁根據其內容建立自定義的用戶概念模式；利用概念模式對網頁進行標記，使概念模式中的欄位與網頁中的信息塊對應起來，建立對應關系並傳給學習模塊以形成抽取規則並存入規則庫；抽取模塊根據規則庫中的抽取規則進行信息抽取並將抽取到的信息按用戶定義的概念模式形成記錄，按領域分類存儲于cache庫中以備用戶查詢；最後，用戶利用查詢功能實現個性化查詢。
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