檢索詞向量 的英文怎麼說

中文拼音 [jiǎnsuǒcíxiàngliáng]
檢索詞向量 英文
term vector
  • 檢 : Ⅰ動詞1 （查） check up; inspect; examine 2 （約束; 檢點） restrain oneself; be careful in one s c...
  • 索 : Ⅰ名詞1 （大繩子; 大鏈子） a large rope 2 （姓氏） a surname Ⅱ動詞1 （搜尋; 尋找） search 2 （要; ...
  • 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...
  • 量 : 量動1. （度量） measure 2. （估量） estimate; size up
  • 檢索 : retrieval; retrieve; search; searching
  1. A method that combines category - based and keyword - based concepts for a better information retrieval system is introduced. to improve document clustering, a document similarity measure based on cosine vector and keywords frequency in documents is proposed, but also with an input ontology. the ontology is domain specific and includes a list of keywords organized by degree of importance to the categories of the ontology, and by means of semantic knowledge, the ontology can improve the effects of document similarity measure and feedback of information retrieval systems. two approaches to evaluating the performance of this similarity measure and the comparison with standard cosine vector similarity measure are also described

    介紹了一種綜合各層級分類類目和對應關鍵詞來構造概念體系並用於改進信息檢索系統效果的方法.為了改進文本聚類的效果，提出了將領域知識本體和文本關鍵詞詞頻相結合的基於餘弦向量的文本相似性測度方法.該本體面向特定領域，將關鍵詞以不同權值對應于各分類類目，通過其語義知識來改進文本相似性測度以及信息檢索系統的效果.進一步給出了對基於本體的相似性測度方法進行效果評價的2種策略以及該方法與經典餘弦向量測度方法的比較結果
  2. Then it presents the design of information push service based on agent by using artificial intelligence technique. a brief introduction of each function module in this system and their internal transaction sequence are followed. the detail design and implement of key parts in each module is also given, which includes setting up user interest model with vector space model, searching information by using word segmenting and searching engine, filtering information using sorting algorithm, ordering information using pagerank algorithm

    本文首先分析了傳統的信息「拉取」方式存在的主要問題以及推送技術的產生;然後結合人工智慧領域的agent技術，提出了基於agent的信息推送服務的總體設計，並簡要闡述了各功能模塊的內部處理流程和思想;接著給出了各模塊的詳細設計與實現，主要包括:利用向量空間模型（ vectorspacemodel ）建立用戶興趣模型，通過分詞並與搜索引擎協作實現信息檢索，採用分類演算法對已檢索的信息進行過濾，用pagerank演算法對信息排序。
  3. After analyzing the principle of keywords and concept retrieval, a new vector space model named sc - vsm based on the semantic concept retrieval was proposed

    摘要對關鍵詞和概念檢索的原理進行分析后，提出了一種基於語義概念檢索的向量空間模型以及該模型與關鍵詞檢索結合的檢索方法。
  4. Experimental results show that the sc - vsm is superior to the vsm, the combinative approach can not only reserve the advantages of keywords retrieval and concept retrieval, but also compensate for their shortcomings

    實驗結果表明，語義概念檢索的向量空間模型的性能優于關鍵詞檢索的向量空間模型；結合檢索方法既能保留關鍵詞檢索和概念檢索的優點，也能彌補各自的不足。
  5. According to such an idea, we propose a new retrieval method that combines xpath and vector space model, named as the vector retrieval model based on xpath. secondly, we make full use of the hierarchical architecture of xml data, and analyze the structure of every document to construct a structure thesaurus, which is designed to navigate the user query and to eliminate the structural conflict

    根據這一思想，作者提出了將xpath語言與傳統的向量空間模型相結合，實現基於簡單xpath路徑的向量檢索演算法來實現對xml文檔的檢索。充分利用xml文檔分類層次體系結構的特點，對于每篇xml文檔分析其文檔結構，並採用聚類學習演算法形成文檔結構類屬詞典，從而實現xml文檔查詢的導航機制和消除文檔結構的異構性。
  6. In vector space model of information retrieval, a text is represented as a weighted vector which is composed of terms weighting of the text

    摘要在信息檢索的向量空間模型中，文本被形式化表示為由詞語權重組成的向量。
  7. Knowledge purification is the key procedure of knowledge acquisition, and machine learning is a effective method to gain wisdom for computers, among which artificial neural network with tutor coached can learn more accurate knowledge by faint structure, and then is a perfect way to deal with misty knowledge by describing and computing intangibly. lt is hard to describe or compute the misty relation of terms and document sort with accurate way. and we can figure out misty knowledge with misty way, so the paper introduces ann into vcm to form a conjoint method vcm ann

    。其中，有導師指導的人工神經網路能夠以模糊的結構學習較為精確的內容，是將模糊的知識進行模糊計算和模糊描述的理想方法。詞條項與文檔類別之間的模糊關系難以用精確的方法進行精確地描述與計算，模糊的知識用模糊的方法能得到較好的解決，因此本文將神經網路應用到信息檢索模型中，將之與向量空間模型相結合，形成了一種改進的向量空間模型vcmann 。
  8. Firstly, the paper introduces the main theoretics and technologies of the web information retrieval. then it applies the spider to realize the information gathering. according to characteristic of uighur language, using uighur stemming based on table searching regular and arithmetic of the combined mode, uighur text segmentation is realized ; using vector space model, the paper switches uighur text information into structured data ; and appling clustering analytical method, these structured text is clustered

    本文首先分析了web信息檢索的主要理論基礎和關鍵技術，然後利用spider信息採集技術，實現了信息檢索的源信息採集;根據維吾爾語詞的特點，利用詞干表查找的維文詞干提取演算法和結合模式的維文詞語組合演算法，對維文網頁文本進行詞特徵表示;採用向量空間模型實現文本信息的結構化表達;使用聚類分析法，對結構化文本信息進行聚類，得到文本分類結果。
  9. Li this part, the thesis first profiles semantic features of each document by employing chinese information processing technology in order to change documents into the form which can be operated with the help of mathematical methods. second, the thesis profiles each user ' s information needs by three ways : 1 ) accepting the information provided by the user himself ; 2 ) watching the user ' s retrieval action ; and 3 ) analyzing web server log. in this module, users are also classified into different categories according to their information needs

    在用戶建模中，系統從三方面獲取用戶信息需求特徵，第一，用戶主動地向系統提供需求信息；第二，系統檢測用戶檢索行為，從用戶檢索詞分析其需求；第三，系統通過分析web訪問日誌，得到用戶的興趣所在及興趣的變化狀況，並進一步利用對用戶訪問文檔內容的分析來追蹤其興趣變化，將用戶興趣同樣表示為興趣特徵向量，聚類相似用戶。
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