句子語義結構 的英文怎麼說

中文拼音 [jùziyǔyìjiēgòu]
句子語義結構 英文
semantic structure of sentences
  • 句 : 句名詞1. [書面語] （指草木初生拳狀的幼芽） tender bud2. （姓氏） a surname
  • 子 : 子Ⅰ名詞1 （兒子） son 2 （人的通稱） person 3 （古代特指有學問的男人） ancient title of respect f...
  • 語 : 語動詞[書面語] （告訴） tell; inform
  • 義 : Ⅰ名詞1 （正義） justice; righteousness 2 （情誼） human ties; relationship 3 （意義） meaning; si...
  • 結 : 結動詞（長出果實或種子） bear （fruit）; form （seed）
  • 構 : Ⅰ動詞1 （構造; 組合） construct; form; compose 2 （結成） fabricate; make up 3 （建造; 架屋） bui...
  • 句子 : sentence
  • 語義 : semanteme; semantics
  • 結構 : 1 （各組成部分的搭配形式） structure; composition; construction; formation; constitution; fabric;...
  1. In the framework of the generative grammar, the government and binding theory provides us evidence of the classification of these sentential types semantically and syntactically : the argument structure and epp determine the obligatory elements at the sentential level, morphological rules and the case theory motivate the production of some derived sentence types

    生成語法管約論框架內分析了劃分的語義和句法依據，謂詞的題元結構和epp決定了句子層面的必有成分，同時形態規則的作用和滿足格理論還會推導出一些衍生句式。
  2. This process includes ellipsis subject detecting and ellipsis subject recovering. we put forward and implement a method to detect subject ellipsis in the semantic structure of a sentence, and a " candidate - selecting " strategy to recover the elliptical subject by syntactic conditions, semantic conditions and context conditions

    處理過程分為主語省略的識別和主語省略的恢復，提出了一種在漢語句子語義結構中識別主語省略的方法和一種從句法、語義和上下文語境多方面綜合判斷的「候選-選擇」主語省略恢復策略。
  3. Firstly, for the errors of text ’ character and word, utilizing neighborship of character or word, check character and word errors by character string co - occurrence probability. secondly, for the errors of syntax of text, according to statistic and analysis of a large - scale contemporary chinese corpus, recognize the predicate focus word and the others sentence ingredient, check the syntax errors. thirdly, for the errors of text ’ semanteme, establishing semantic dependency relationship tree based on hownet knowledge, presents a method that based on semantic dependency relationship analysis to compute sentence similarity, check the semantic errors

    對于文本字詞錯誤的檢查，本文主要利用了字詞二元接續關系，根據同現概率檢查文本字詞錯誤;對于文本語法錯誤的檢查，本文利用教研室已有的一個大規模語料庫，通過對語料庫進行統計分析，獲得語法查錯所需要的語言規律和知識，利用謂語中心詞識別和其他句子成分識別的方法，檢查文本語法結構上的錯誤;對于文本語義錯誤的檢查，本文主要利用知網知識得到語義依存樹，通過對句子的有效搭配對的相似度計算檢查語義錯誤。
  4. Many approaches developed offer approximation solutions, including principle - based, such as purely syntactic ones, semantic and pragmatic ones, and statistics - based ones. in this paper, we put forward a modal to resolve anaphora ( including empty pronoun ) based on the characteristics of chinese. in this method, we apply some corresponding rules on the sentences after their patterns distinguished in the text, then analyze the semantic relation of the components of the sentence and form a corresponding semantic network, get rid of some candidates according to the co - constrain of the nodes in that network, put every component which refers to people into forward - looking centers, ordered by their semantic role, ascertain the hierarchy of sentences, analyze the transition types resulted by the scheme of different resolutions, finally, choice the most possible scheme of resolution according to the precedence of the types

    本文針對漢語的特點建立了一個包括零代詞在內的代詞消解模型:在語義結構文法思想的指導下，對文本中的句子模式識別后，採用相應的規則式，然後分析句中成分之間的表層語義條件並產生相應的語義網路，利用語義網路中結點間的相互約束對代詞的某些候選先行詞進行排除，並且把每句中指人的語言成分放到前向中心列表中，以它們所充當的語義成分為排序依據，確定句子的層次結構，最後依據中心理論分析每個候選先行詞對代詞的不同消解所造成的層次相鄰的句子之間的過渡類型，利用過渡類型的優先順序對代詞的候選先行詞進行擇優。
  5. It process documents not only based on latent semantic analysis, but also based on text multilevel dependency structure. the method first analysis the latent semantic structure of texts, make single value decomposition on text - matrix, reconstruct the semantic matrix ; then a method based on text multilevel dependency structure is adopted, deeply analysis the content of the semantic matrix, abstract the important sentences to generate abstraction and make up the shortage of latent semantic analysis on structure and syntax

    首先通過對文本進行潛在語義分析，對文本矩陣進行相應的奇異值分解，重構語義矩陣;然後採用基於篇章多級依存結構的文摘分析方法，對重構的語義矩陣表示的文本內容進行深入的分析，抽取重要的句子生成文摘，這樣就彌補了潛在語義分析在詞法和句法分析上的不足;同時過濾和去除了語義噪音，縮小了問題的規模。
  6. This article tries to tell the difference in meaning and distinguish between right and wrong in sentences of the same structure by analysing their deep structure, grammar and also by inference

    摘要通過深層結構的剖析，邏輯推理和語法分析，揭示某些結構相同的句子的正誤或意義差別。
  7. Ccm has several features : systematic, practical, dynamic, structural and expandable. and the context knowledge of ccm is made up of syntactic information, semantic information and coherent information. so it is useful for chinese analysis

    Ccm具有系統性、實用性、動態性、結構性和可擴充性的特點，其上下文語境知識包含句法信息、語義信息和句子之間的連貫信息，為分析漢語句子與上下文語境的關系提供了較好的支持。
  8. Disagreement of english syntax structure amp; analysis of exclusive method

    英語句子結構歧義及其排除方法分析
  9. The " binding " method calls an evaluation function in syntactic parsing rules. the function makes pattern matching between the current components and the pattern rules, and then returns an evaluation mark thai is used to help decide whether the current reduction succeeds or fails. so this method can avoid multiple ambiguous results

    在句法分析過程中進行的「約束」語義排歧的主要思想是在句法分析的規則中調用一個評價函數，該函數把當前待歸約產生式中各個子成分的搭配情況與模式庫中的各搭配結構進行匹配運算，並返回最相似的匹配結果及對應的評價值。
  10. The schema, which described the text structure of operation document, is realized by atn. the rules in the sentence aggregation are described in the production way. sentence structure classes and resource trees are adopted to depict syntax structure of sentences, which have similar semanteme

    利用schema描述文書文本結構，並用atn來實現schema ；用產生式方法描述優化聚合規則，用句子結構類和資源樹描述語義與句子結構的對應關系以及用復雜特徵集描述句法規則和詞匯。
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