本文選題：語篇測量　切入點：語篇體裁　出處：《東北農(nóng)業(yè)大學》2017年碩士論文　論文類型：學位論文

【摘要】：近十年來,隨著計算機科學理論的發(fā)展,計算語言學在機器翻譯、語音識別、人機交互等諸多方面發(fā)展迅猛。但是如今的機器翻譯或語音識別等人工智能產(chǎn)品仍然存在著自身局限性。以機器翻譯為例,它對于大篇幅的整句或者邏輯語義相對復雜的文本,翻譯質(zhì)量令人堪憂。其根本原因在于機器對于語篇的理解是建立在代表語言元素的二進制信息和簡單的算法基礎(chǔ)上的;而人對于語篇的理解則是建立在對于小句關(guān)系的理解之上的。另一方面,丁建新、陳安玲等人對大量語篇體裁的統(tǒng)計研究明確了不同語篇體裁中小句關(guān)系分布特征具有不同的特點,這為基于小句關(guān)系的語篇體裁鑒別提供了理論基礎(chǔ)。因此本論文以韓禮德系統(tǒng)功能語言學中的小句關(guān)系為切入點,在廣泛文獻調(diào)研的基礎(chǔ)上,充分吸收韓禮德系統(tǒng)功能語言學中關(guān)于小句復合體系統(tǒng)理論的優(yōu)點,論述了該理論下小句關(guān)系分類框架存在的不足,并結(jié)合國內(nèi)學者程曉堂對小句關(guān)系分類框架的改進意見,首次提出了小句關(guān)系特征矩陣和語篇相關(guān)度的概念,并在這兩個概念的基礎(chǔ)上提出了基于小句關(guān)系定量分析的語篇測量方法。小句復合體作為語篇中最高級別的語法單位,其內(nèi)部各個小句之間相互作用,存在多種復雜的關(guān)系,這些關(guān)系蘊含了豐富的信息,而小句關(guān)系特征矩陣作為語篇中小句關(guān)系分布特征的直觀體現(xiàn),我們可以從中解讀出關(guān)于該語篇的豐富的語言學意義。語篇相關(guān)度則是從統(tǒng)計學的角度給出了不同語篇體裁之間相關(guān)程度的量化分析方法。由語篇相關(guān)度概念引申,我們給出語篇差異這個概念,它從另一個側(cè)面反映了不同語篇體裁之間的小句關(guān)系分布特征的差異性,并且能夠直觀地給出具體的差別所在。這些概念和方法的提出,使得我們可以借此對機器進行大規(guī)模的語篇數(shù)據(jù)訓練,從而實現(xiàn)大規(guī)模語篇材料的自動體裁判別和分類的功能。本論文以定量分析為主,結(jié)合統(tǒng)計分析、案例分析、演繹推理、綜合歸納、文獻檢索等諸多研究方法,以不同語篇作為樣本數(shù)據(jù),其對應的小句關(guān)系特征矩陣作為模型參數(shù)進行研究。首先對語篇中小句關(guān)系類別進行分析得到小句關(guān)系特征矩陣,進而對小句關(guān)系特征矩陣作誤差校正預處理和統(tǒng)計學相關(guān)性檢驗,最后得到語義相關(guān)度、修辭相關(guān)度和投射相關(guān)度的加權(quán)平均值,即語篇相關(guān)度,可以以此定量地表示不同語篇之間相似性程度。這樣便建立了一種基于小句關(guān)系定量分析的系統(tǒng)化的語篇測量方法。經(jīng)過多個語篇案例的實際檢驗,驗證結(jié)果與預期符合很好,充分說明了該理論的合理性、正確性和可行性。本論文所提出的基于小句關(guān)系定量分析的語篇測量方法不僅可以在微觀上推斷出語篇本身蘊含的豐富的語言學信息,而且可以在宏觀上得出不同語篇體裁之間的相似性程度,并給出定量化的描述。該語篇測量方法在機器語篇分析中具有很強的可操作性和應用價值,為科學、客觀、系統(tǒng)的語篇分析研究開拓了新的研究思路。
[Abstract]:In the past ten years, with the development of computer science, computational linguistics at Machine Translation, voice recognition, human-computer interaction and other aspects of the rapid development. But now the Machine Translation voice recognition or artificial intelligence products still have their own limitations. In the case of Machine Translation, it is for a large text or sentence semantic logic relatively complex that is the translation quality is worrying. The fundamental reason is that the machine is built to represent language elements of binary information and simple algorithm based on the understanding of text; and for discourse understanding is based on the understanding of the relationship between clauses. On the other hand, Ding Jianxin, Chen Anling, et al. Clear different genre and sentence distribution has different characteristics of a large number of statistical research on the genre of discourse, the clause relation of genre identification based on The theoretical basis of this paper. The clause relation Hallidy in systemic functional linguistics as the starting point, on the basis of extensive literature investigation, fully absorb Hallidy in systemic functional linguistics about the clause complex system theory discusses the advantages, shortcomings of the theory of clause relation classification framework, and combined with the suggestions for improvement the domestic scholar Cheng Xiaotang framework of clause relation classification, first proposed the clause relation feature matrix and discourse the concept of correlation degree, and puts forward the clause relation of quantitative analysis in the measurement method based on discourse on the basis of the two concepts. The clause complex as a grammatical unit of the highest level in the discourse, the each interaction between clauses, there are several complicated relationship, the relationship contains abundant information, and the clause relation characteristic matrix as a discourse and sentence distribution straight The conception, we can out of the discourse rich linguistic meaning in discourse interpretation. Correlation is to quantify the degree of correlation between different genre analysis method is given from the perspective of statistics. Composed of discourse related concept, we give the concept of discourse differences, it reflects the difference between different genres of clause relation distribution from another side, and can directly give specific difference. Put forward these concepts and methods, so that we can take the machine text data of large-scale training practice, so as to realize the automatic identification and classification of large-scale genre text materials function. This study is mainly based on the quantitative analysis, combined with statistical analysis, case analysis, deductive reasoning, summarizing, literature retrieval and other research methods, as the sample data in different texts, the corresponding clause To study the relationship between the characteristic matrix as model parameters. Firstly, analysis of the clause relation feature matrix of discourse relations of small sentence categories, clause relation feature matrix error correction preprocessing and statistical correlation test, and finally get the semantic relevance, rhetoric correlation and correlation projection weighted average value, namely, discourse this correlation, can quantitatively represent the degree of similarity between texts. It established a small sentence systematic quantitative analysis of the relation of discourse measurement method based on multiple discourse. Through actual case inspection inspection, verification and expected results are in good agreement, fully illustrates the rationality of the theory. The correctness and feasibility. This paper proposed the clause relation of quantitative analysis measurement method based on discourse can not only infer the discourse itself contains rich linguistic information in the micro level, and And that the degree of similarity between different genres at the macro level, and gives a quantitative description of the discourse. Measurement methods in machine in discourse analysis has strong maneuverability and application value, scientific, objective, discourse analysis system research has opened up new research ideas.

【學位授予單位】：東北農(nóng)業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：H05

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