本文關(guān)鍵詞：漢語動詞語義特征建模與分析　出處：《中國康復(fù)醫(yī)學(xué)雜志》2016年04期 　論文類型：期刊論文

  更多相關(guān)文章： 動詞概念 論元結(jié)構(gòu) 語義特征訓(xùn)練 言語語言治療 康復(fù)

【摘要】：目的:通過構(gòu)建漢語動詞語義特征常模,為臨床言語治療提供量化和可視化的語義特征數(shù)據(jù)庫。方法:選擇30個日常生活常用動詞作為刺激詞,采集健康人的語義特征,并進行條目編碼,然后根據(jù)漢語語義特征分型方案對其進行分類。統(tǒng)計軟件采用R軟件進行數(shù)據(jù)可視化和統(tǒng)計檢驗。結(jié)果:(1)動詞的語義特征以功能用途類顯著。(2)動詞首位秩次的語義特征以功能用途類顯著。(3)一論元結(jié)構(gòu)動詞以內(nèi)省特征顯著。結(jié)論:根據(jù)漢語語義特征數(shù)據(jù)建立的模型可以有效反映概念語義結(jié)構(gòu),有助于根據(jù)量化指標提取語義訓(xùn)練素材。
[Abstract]:Objective: to provide a quantitative and visual semantic feature database for clinical speech therapy by constructing the Chinese verb semantic feature norm. Methods: 30 verbs commonly used in daily life were selected as stimuli. The semantic features of healthy people were collected and the items were coded. Then it was classified according to the Chinese semantic feature classification scheme. The statistical software R was used to visualize and test the data. The results showed that the semantic feature of the verb was significant in the functional use category. The semantic features of the first rank of verbs are significant within the verb of functional use class. Conclusion: the model based on the semantic feature data of Chinese can effectively reflect the conceptual semantic structure. It is helpful to extract the semantic training material according to the quantization index.
【作者單位】： 南京醫(yī)科大學(xué)附屬第一醫(yī)院康復(fù)醫(yī)學(xué)科;
【基金】：江蘇省科技支撐計劃(BE2012675) 國家自然科學(xué)基金資助項目(81171854)
【分類號】：R493
【正文快照】： 人類通過不同的語句進行信息傳遞及交流,而成。其中動詞在句中的作用尤為關(guān)鍵,因其不僅攜語句是通過不同性質(zhì)的詞語(如名詞、動詞、形容詞帶重要信息,而且在句子結(jié)構(gòu)形成中有重要作用[1]。等)在句中承擔(dān)不同的語法成分而合理的組合而也就是說,動詞通常攜帶論元,不僅表現(xiàn)出語義，

本文編號：1408893