本文選題：研討系統(tǒng) + 研討文本　； 參考：《湖北工業(yè)大學(xué)》2017年碩士論文

【摘要】：群體研討支持系統(tǒng)是綜合集成研討廳的一個重要組成部分,其作用是支持群體成員在線交流自己的意見并對這些意見進行綜合分析。在研討過程中,會產(chǎn)生大量的發(fā)言文本信息,如果不對這些文本進行分析處理,則不利于研討參與者識別不同發(fā)言內(nèi)容之間的邏輯關(guān)系。目前,對研討信息的分析處理多采用人工智能領(lǐng)域中的辯論推理方法,即通過分析發(fā)言結(jié)點之間的攻擊或支持關(guān)系及攻擊或支持的強度確定發(fā)言結(jié)點的可接受性或發(fā)言結(jié)點的共識值。但是,在基于文本的研討過程中,研討文本之間沒有明確的攻擊與支持關(guān)系,這時群體共識隱藏于研討文本內(nèi)容之中,這就需要對研討文本內(nèi)容進行分析處理。針對這個問題,本文提出對多研討文本內(nèi)容進行分析處理并提取共識的方法,并將該方法運用于群體研討支持系統(tǒng)之中。具體工作如下:1、提出一種基于多文檔摘要的研討文本分析方法。該方法首先對研討文本進行預(yù)處理;然后利用啟發(fā)式聚類算法對預(yù)處理后的文本進行聚類分析,形成多個文本簇,并采用多文檔摘要算法提取各文本簇摘要;最后采用D3技術(shù)展示聚類結(jié)果和摘要內(nèi)容。2、在分析經(jīng)典的Text Rank算法的基礎(chǔ)上,提出一種改進的Text Rank文本摘要算法。該算法的核心思想是將句子位置、關(guān)鍵詞句子和句子長度等信息加入到句子權(quán)重計算中,同時給出了改進后句子相似度的計算方法并通過實驗證明該算法是可行的。3、開發(fā)一個基于Web的研討文本分析子系統(tǒng),并把該子系統(tǒng)整合到實際的研討系統(tǒng)中。用一個研討實例中的研討文本集進行實驗分析,通過調(diào)節(jié)聚類算法中的閾值,可以得到不同的聚類結(jié)果和文本簇的摘要。實驗證明,本文所提出的基于多文檔摘要研討文本分析方法是可行的,能有效促進專家思維收斂,提高研討效率。
[Abstract]:Group discussion support system is an important part of the integrated research hall. Its function is to support group members to exchange their opinions online and analyze them synthetically. In the process of discussion, a large amount of speech text information will be produced. If these texts are not analyzed and processed, it will be unfavorable for the participants to identify the logical relationship between different speeches. At present, the reasoning method in the field of artificial intelligence is mostly used in the analysis and processing of the discussion information. In other words, the acceptability of the speaker node or the consensus value of the speaker node are determined by analyzing the attack or support relationship between the speakers and the intensity of the attack or support. However, in the process of text-based research, there is no clear attack and support relationship between the study texts, so the consensus is hidden in the content of the research text, which needs to be analyzed and processed. In order to solve this problem, this paper puts forward a method of analyzing and processing the contents of multi-discussion texts and extracting consensus, and applies this method to the group discussion support system. The main work is as follows: 1. A research text analysis method based on multi-document summary is proposed. The method firstly preprocesses the research text, then uses the heuristic clustering algorithm to cluster the preprocessed text to form multiple text clusters, and uses the multi-document summarization algorithm to extract each text cluster summary. Finally, D3 technique is used to show the clustering results and summary content. Based on the analysis of the classical Text Rank algorithm, an improved Text Rank text summary algorithm is proposed. The key idea of the algorithm is to add sentence position, key word sentence and sentence length into sentence weight calculation. At the same time, the improved method of sentence similarity calculation is given, and the algorithm is proved to be feasible by experiments. A discussion text analysis subsystem based on Web is developed, and the subsystem is integrated into the actual research system. The experimental analysis is carried out with a discussion text set in an example. By adjusting the threshold in the clustering algorithm, different clustering results and abstracts of text clusters can be obtained. The experiments show that the method proposed in this paper is feasible and can effectively promote the convergence of expert thinking and improve the efficiency of research.
【學(xué)位授予單位】：湖北工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP391.1

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