本文選題：連動句 + 事件語義類��； 參考：《江蘇科技大學(xué)》2017年碩士論文

【摘要】：常識知識獲取是人工智能領(lǐng)域一個重要研究課題也是一個長期存在的挑戰(zhàn)。其目標是構(gòu)建面向應(yīng)用的大規(guī)模常識知識庫以實現(xiàn)真正的智能系統(tǒng)。事件的前提常識和后果常識作為兩種重要的常識知識,在自動問答、自然語言理解、信息檢索等領(lǐng)域都具有極大的應(yīng)用價值。但是由于常識知識具備隱含性、泛在性和基礎(chǔ)性等特點,機器無法自動獲取大量隱含的常識知識。連動句是現(xiàn)代漢語中一種常見句式,每個連動句都包含兩個或兩個以上謂語動詞且這兩個謂語動詞是相互依賴的,它們具備目的、因果、方式等語義關(guān)系。一個謂語動詞即一個事件,因而連動句是描述多個事件的特殊句式。連動句中的事件具有多種語義關(guān)系,所以連動句蘊含了豐富的事件常識。連動句在人類描述語言中大量存在,句式簡單且有模式可循。因此,連動句可作為一個大規(guī)模易獲取的知識源,為海量常識的獲取提供契機。針對上述問題,本文系統(tǒng)地研究了從漢語連動句中獲取前提常識和后果常識的理論和方法,具體研究內(nèi)容包括以下三個方面:首先研究連動句識別方法,本文給出一種基于規(guī)則與統(tǒng)計的漢語連動句識別方法。為了實現(xiàn)連動句自動識別,該方法從連動句形式特征和語義角色兩個角度構(gòu)建基礎(chǔ)規(guī)則庫,利用統(tǒng)計學(xué)方法計算兩個謂語動詞之間的中間詞的特征詞性是被動名詞的概率。實驗表明,基于規(guī)則和統(tǒng)計的方法準確率達到75.48%,相較于僅基于規(guī)則的識別方法提高了14.46%。然后研究連動文法構(gòu)建方法,本文以事件語義類文法為基礎(chǔ),利用連動句的語義特征和句法結(jié)構(gòu),構(gòu)建了自動生成連動文法規(guī)則,為基于連動句的常識獲取提供理論基礎(chǔ)。最后研究基于連動句的常識獲取方法,本文給出了四種基于漢語連動句的常識獲取方法,分別是:通過連動詞對的語義獲取常識、通過連動文法的事元角色獲取常識、通過常識知識角度獲取常識和通過連動句的類型獲取常識。然后,基于以上四種方法設(shè)計了七種問題模板及交互腳本,以交互的方式提問并引導(dǎo)知識工程師獲取常識。為了論證交互過程的合理性,本文給出了基于二項分布假設(shè)檢驗的定量評估模型來驗證交互過程的可接受性和有效性。實驗表明,利用本文方法獲取常識,知識正確率達到92.5%。
[Abstract]:The acquisition of common sense knowledge is an important research topic in the field of artificial intelligence and a long-standing challenge. The goal is to build an application oriented large scale knowledge base to achieve real intelligent systems. The precondition of the event and the common sense of the consequences are two important common sense knowledge, in automatic question and answer, natural language understanding, and information inspection. The fields of cable are of great value in application. However, because of the implicit, ubiquitous and basic characteristics of common sense knowledge, the machine can not automatically obtain a large number of implicit knowledge. The sentence is a common sentence in modern Chinese, each of which contains two or more than two predicate verbs and the two predicate verbs are the phase. Interdependence, they have semantic relations, such as purpose, causation and way. A predicate verb is an event, so the verb is a special sentence pattern describing many events. The event in the sentence has a variety of semantic relations, so the connection sentence contains a lot of common sense of events. Therefore, the model can be used as a large and easy access knowledge source, which provides an opportunity for the acquisition of mass common sense. In this paper, this paper systematically studies the theory and method of obtaining the common sense and common sense of the precondition from the Chinese serial sentence. The specific research contents include the following three aspects: first of all, the study of the serial sentence. In order to realize the automatic recognition of continuous sentences, this method constructs the basic rule library from two angles of the form feature and the semantic role of the continuous verb sentence. The statistical method is used to calculate the characteristics of the middle word between the two predicate verbs, which is the probability of the passive noun. The experiment shows that the accuracy of the method based on rules and statistics is up to 75.48%. Compared to the rule based recognition method, the method is improved by 14.46%. and then the construction method of continuous grammar is studied. Based on the semantic feature and syntactic structure of the syntactic sentence, this paper constructs the automatic generation of continuous dynamic grammar rules, which is based on connection. The common sense acquisition of dynamic sentences provides a theoretical basis. Finally, the common sense acquisition method based on continuous sentences is studied. In this paper, four methods of common sense acquisition based on Chinese continuous verb are given. Then, the seven problem templates and interactive scripts are designed based on the above four methods, and the knowledge engineers are asked to interactively ask and guide the knowledge engineers to obtain common sense. In order to demonstrate the rationality of the interaction process, this paper gives a quantitative evaluation model based on the two distribution hypothesis testing to verify the acceptability of the interactive process. The experiments show that this method can acquire common sense and the accuracy of knowledge is 92.5%..

【學(xué)位授予單位】：江蘇科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;TP391.1

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