發(fā)布時間：2018-07-12 10:39

  本文選題：虛假信息檢測 + 矛盾檢測��； 參考：《哈爾濱工業(yè)大學》2017年博士論文

【摘要】：文本虛假信息檢測是自然語言處理領域的熱點問題之一,其目的在于從網(wǎng)絡文本中識別、過濾不真實或不正確的信息。虛假信息檢測研究的意義在于識別虛假信息及不可靠的信息源,避免人們在認識事物及購物消費時受到虛假信息的誤導。網(wǎng)絡中的文本信息可以分為客觀信息及主觀信息�？陀^信息是指對事物的客觀描述,其真實信息在內(nèi)容上具有唯一性,與真實信息相矛盾的信息即為虛假信息。主觀信息指人們的主觀感受或經(jīng)歷體驗,其真實信息在內(nèi)容上不具有唯一性。主觀信息的真實性與虛假性的區(qū)別在于是否來自于真實的用戶經(jīng)歷。針對客觀信息及主觀信息的虛假信息檢測問題,研究者需要針對二者不同的特點分別進行檢測技術的研究。當該問題的研究場景中還包括信息源、用戶反饋等外部數(shù)據(jù)時,結(jié)合外部數(shù)據(jù)檢測虛假信息也是有價值的研究方向。在虛假信息檢測問題中,一個核心問題是如何對虛假信息的文本內(nèi)容及外部數(shù)據(jù)進行有效地表示�；�于機器學習的方法在虛假信息檢測任務上應用最為廣泛,而特征表示在很大程度上決定了機器學習算法的性能。由于虛假信息多來自于人為編造,編造者在內(nèi)容及寫作風格上模仿真實信息,使得虛假信息具有很強的迷惑性。特征設計依賴專家經(jīng)驗,虛假信息的強迷惑性對特征設計帶來了挑戰(zhàn)。表示學習能夠從數(shù)據(jù)中自動學習潛在的規(guī)律特征,對信息有加工和抽象的能力。表示學習方法為虛假信息檢測研究帶來了新的機遇。本課題在虛假信息檢測的背景下研究網(wǎng)絡文本及外部數(shù)據(jù)的表示學習方法,從而提升虛假信息的檢測性能。本課題的研究內(nèi)容從以下四個方面展開:1.本文提出了基于依存分析及矛盾詞向量學習的方法進行矛盾關系檢測。針對客觀信息中虛假信息與真實信息內(nèi)容語義相互矛盾的現(xiàn)象,提出了基于矛盾檢測的虛假信息檢測方法。語義矛盾現(xiàn)象中對于矛盾詞的理解和檢測是一個難點,WordNet等詞匯資源無法識別矛盾詞對相互矛盾的語義關系。本文通過學習針對矛盾檢測任務的特定詞向量表示,并將其運用到神經(jīng)網(wǎng)絡模型中,有效提升了矛盾檢測任務的性能。從而識別句子間的矛盾關系,發(fā)現(xiàn)虛假信息。2.本文提出了基于句子權(quán)重的虛假信息表示學習方法進行虛假信息檢測。由于缺少佐證,主觀信息的虛假性較難判斷。然而謊言在遣詞造句中仍與真實信息間有一定的區(qū)分度,存在一些潛在的規(guī)律及特點。本文運用文檔語義表示學習的方法挖掘數(shù)據(jù)內(nèi)部的規(guī)律特點。由于信息中每個句子對信息的虛假性判斷具有不同的重要性,本文提出了結(jié)合句子權(quán)重學習文檔表示的虛假信息檢測方法。此種方法通過文檔表示學習替代了傳統(tǒng)的特征工程方法。將句子權(quán)重計算與文檔表示學習相結(jié)合,有效提升了系統(tǒng)的檢測性能。3.本文提出了融合信息源可靠度的虛假信息表示學習方法進行虛假信息檢測。人們在日常生活中遇到的很多信息如航班的登機時間等,存在著不同信息源的信息相互沖突矛盾的情況。由于信息的內(nèi)容簡單平實,真實信息與虛假信息在語言、語法等文本特征上的區(qū)分度小,不易檢測虛假信息。本文將信息源的可靠度看做待檢測信息的外部知識,通過記憶網(wǎng)絡模型將信息源可靠度與信息的可信度相結(jié)合進行迭代計算,預測信息的虛假性。4.本文提出了融合用戶反饋的虛假信息表示學習方法進行虛假信息檢測。在社交媒體上,面對一條信息(如微博或Tweet),其他用戶會在該信息的轉(zhuǎn)發(fā)微博中表達支持、反對或質(zhì)疑的觀點或態(tài)度。轉(zhuǎn)發(fā)微博作為一種用戶反饋包含了用戶對所轉(zhuǎn)發(fā)信息的虛假性判斷,是一種群體智慧的體現(xiàn)。本文運用基于注意力機制的表示學習方法,對源微博及用戶反饋信息進行表示學習及表示的語義合成。在模型中運用注意力機制自動對用戶反饋信息進行權(quán)重分配,有效提升了對該問題的檢測性能�？傮w來講,本論文利用表示學習在語義表示上的通用性,深入地研究了其在客觀信息、主觀信息、與信息源結(jié)合及與用戶反饋結(jié)合的不同研究場景下對虛假信息檢測問題的應用。希望本研究能夠?qū)μ摷傩畔�檢測及自然語言處理領域的學者提供一些參考。
[Abstract]:The detection of false information in text is one of the hot issues in the field of Natural Language Processing. The purpose of the false information detection is to identify the untrue or incorrect information from the network text. The significance of the false information detection research is to identify false information and unreliable information sources, and to avoid people receiving false information when they know things and shopping. The text information in the network can be divided into objective information and subjective information. Objective information refers to the objective description of things, the real information is unique in the content, and the information contradicting the real information is false information. Subjective information refers to people's subjective feelings or experience experience, and the real information is not in content. Uniqueness. The difference between the authenticity of the subjective information and the falsehood lies in whether it comes from the real user experience. For the false information detection of the objective information and the subjective information, the researchers need to study the detection techniques for the two different characteristics. It is also a valuable research direction to detect false information with external data. In the problem of false information detection, a core problem is how to effectively express the text content and external data of false information. The method based on machine learning is the most widely used in the false information detection task, and the feature is expressed. To a great extent, the performance of the machine learning algorithm is determined. Because the false information comes from the artificial creation, the creator imitates the real information in the content and writing style, making the false information very puzzling. The feature design relies on the expert experience. The strong and puzzling of the false information brings challenges to the feature design. It is able to automatically learn potential regularity features from data and have the ability to process and abstract information. The presentation of learning method has brought new opportunities for the research of false information detection. This subject studies the expression learning method of network text and external data in the background of false information detection so as to improve the detection performance of false information. The research content of the question is carried out from the following four aspects: 1. this paper puts forward the contradiction relationship detection based on the method of dependency analysis and the vector learning of contradictory words. In view of the contradiction between the false information and the content semantics of the real information in the objective information, a false information detection method based on the contradiction detection is proposed. The understanding and detection of contradictory words is a difficult point. WordNet and other lexical resources can not identify the contradictory semantic relations of contradictory words. By learning the specific word vector of the contradiction detection task, this paper applies it to the neural network model and effectively improves the performance of the spear shield detection task, thus identifying the sentences. Paradox relationship and false information.2. this paper puts forward the false information based on the sentence weight to express the false information detection. Because of the lack of evidence, the falsehood of the subjective information is difficult to judge. However, there are some differences between the lies and the real information in the words and sentences, and there are some potential laws and characteristics. The method of using document semantics to express learning is used to excavate the regularity of the internal data. Since each sentence in the information has different importance to the false judgment of information, this paper proposes a false information detection method which combines the sentence weight learning document representation. This method has replaced the traditional feature engineering by means of document representation. Method. Combining the sentence weight calculation with the document representation learning, it effectively improves the detection performance of the system.3.. This paper proposes a false information representation learning method which combines the reliability of the information source to detect the false information. Many information, such as the boarding time of the flight, are encountered in the daily life, and there are different information sources. Because the content of information is simple and simple, the distinction between real information and false information in language, grammar and other text features is small, and it is not easy to detect false information. In this paper, the reliability of the information source is regarded as the external knowledge of the information to be detected, and the reliability of the information source is trusted and the information is trusted by the memory network model. .4. in this paper presents a false information that combines user feedback with a learning method for false information detection. In social media, in the face of a message (such as micro-blog or Tweet), other users will express support, objection or attitude in the forwarding micro-blog of the information. The forward micro-blog, as a user feedback, contains the false judgment of the users' forwarded information. It is an embodiment of group intelligence. This paper uses a representation learning method based on attention mechanism to represent the semantic synthesis of the source micro-blog and the user feedback information. In the model, the attention mechanism is used to automatically be used for users. The weight allocation of feedback information can effectively improve the detection performance of the problem. In general, this paper makes use of the generality of expression learning in semantic representation, and deeply studies the application of the false information detection problem in the different research scenes in the different research scenes, which are the objective information, the subjective information, the combination of the information source and the information source and the user feedback. It is hoped that this study can provide some reference for false information detection and scholars in the field of Natural Language Processing.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TP391.1

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