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研究生:柳沃辰
研究生(外文):Wo-ChenLiu
論文名稱:基於討論參與度與非正規網路語言增強模型之微網誌內容融合系統
論文名稱(外文):A Microblog Content Fusion System Based on User Participation Degree and Enhanced NIL Model
指導教授:郭耀煌郭耀煌引用關係
指導教授(外文):Yau-Hwang Kuo
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:46
中文關鍵詞:微網誌使用者生成的內容短文過濾內容融合
外文關鍵詞:MicroblogUser-generated ContentShort TextFilteringContent Fusion
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  • 被引用被引用:0
  • 點閱點閱:161
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  在微網誌上,使用者經常使用簡短的文字(例如:縮寫)以及一些非文字的元素(例如:超連結、影片、表情附號),來克服微網誌內容長度上的限制。然而,在微網誌的回應當中經常包含了許多含混不清、沒有必要或與原文主題無關的訊息,這些訊息將會影響我們分析的結果。除此之外,微網誌的文章與回應當中也經常包含了許多非正規網路語言(NIL),像是拼錯的字、諧音字以及縮寫。本論文提出了一個新方法,對每一篇文章進行以下步驟:過濾與原文無關的回應,並基於討論參與度找出最大討論群(MDG)。根據找出的最大討論群(MDG)作為文章分數計算的依據。文章經計算後挑選出排名較前面的文章,這些被挑選出來的文章,結合本論文修改過的非正規網路語言模型(NIL Model)與語彙鏈模型(Lexical Chain Model),從中挑選出有意義且重要的關鍵詞。為了使最後融合的內容更為豐富,我們從多元的的微網誌平台中挑選相關的內容進行內容融合。
  實驗的部分,本論文的實驗從Plurk、Facebook抓取了三組關鍵字的資料,分別為:“林書豪”、“馬英九” 、“蔡英文”,並且建立了與關鍵字相關的ENIL字典。我們也比較了ENIL模型與中研院斷詞系統(CKIP)的斷詞精確度。與中研院斷詞系統(CKIP)比較的實驗結果顯示,本論文能增進7.4%~17.5%的斷詞精確度。整體效能評估方面,我們利用NDCG來評量使用者對於結果與查詢詞之間關聯性的滿意度,結果顯示大部分的使用者認為我們的系統具備提供良好融合結果的能力。
Microblog users publish their opinions by using condensed text with some non-textual contents because of the limitation of content length. Moreover, user-generated content often includes chaotic messages, useless information or unrelated information to the theme of original post. Microblog posts and responses also contain Network Informal Language (NIL) such as abbreviations, misspelled and phonetic words and. In this paper, a novel approach of Maximum Discussion Group Detection (MDGD) from each post and its responses is proposed. Briefly, the MDGs with higher user participation degree are selected to extract the significant terms from unconventional expressions of microblog posts by modified NIL and Lexical Chain models. To enrich the fusion results, we refer the related contents from multiple microblog platforms according to the previous extracted terms.
In the experiments, we use test data set collected from the microblog platforms on Plurk and Facebook which includes the terms of “林書豪”, “馬英九” and “蔡英文”. Then, the NIL dictionary is constructed for ENIL model. Comparing with CKIP, the segmentation results indicate that the precision of ENIL improved 7.4% to 17.5% significantly. Finally, NDCG metrics is used to evaluate the user satisfactions of fusion results. The results of user satisfactions show that our system is capable to provide qualified fused results.

List of Tables ................................................VIII
List of Figures ...............................................IX
Chapter 1 Introduction ........................................1
1.1 Motivation ................................................1
1.2 Contributions .............................................2
1.3 Organization ..............................................3
Chapter 2 Background and Related Work .........................4
2.1 Summarization .............................................4
2.1.1 Document Summarization...................................4
2.2 Segmentation ..............................................5
2.3 Microblog .................................................9
Chapter 3 Multi-Feature Analysis for Microblog Content Fusion .10
3.1 Behavior-based Feature Extraction .........................11
3.2 Feature-based Filtering ...................................12
3.3 Maximum Discussion Group Detection ........................14
3.4 A Novel Term Extraction Method ............................19
3.4.1 Notations ...............................................20
3.4.2 Enhanced Network Informal Language Model ................20
3.4.3 Word Segmentation........................................22
3.4.4 Term Frequency Weighting ................................22
3.4.5 Singular Vector Decomposition ...........................24
3.4.6 Candidate Posts Selection ...............................25
3.5 Multiple Post Selection ...................................26
3.6 Multi-source Fusion .......................................27
3.7 Time Complexity Analysis ..................................32
Chapter 4 Experiment ..........................................33
4.1 NDCG ......................................................33
4.2 Results and Analysis ......................................33
4.2.1 Parameters ..............................................33
4.2.2 Feature-based Filtering .................................34
4.2.3 Maximum Discussion Group Detection ......................35
4.2.4 A Novel Keyword Extraction Method .......................36
4.2.5 Multi-source Fusion .....................................38
Chapter 5 Conclusion and Future Work ..........................41
References ....................................................42
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