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研究生:周宥宇
研究生(外文):Yu-Yu Chou
論文名稱:語音文件摘要:使用結構性支撐向量機、領域調適、抽象式摘要
論文名稱(外文):Spoken Document Summarization : with Structural Support Vector Machine,Domain Adaptation and Abstractive Summarization
指導教授:李琳山李琳山引用關係
指導教授(外文):Lin-shan Lee
口試委員:王小川簡仁宗陳信宏鄭秋豫
口試日期:2013-06-24
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電信工程學研究所
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:2013
畢業學年度:101
語文別:中文
論文頁數:86
中文關鍵詞:語音文件摘要領域調適結構性支撐向量機韻律特徵
外文關鍵詞:Spoken Document SummarizationDomain AdaptationStructural Support Vector MachineProsodic features
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本論文之研究主題為語音文件自動摘要,包括使用結構性支撐向量機、領域調適並接上抽象式摘要的新架構,並以中文課程錄音作為實驗語料,包含語音訊號以及語音辨識轉寫~(Transcription)~。我們首先使用語意、韻律、相似度三大類特徵以不同的機器學習模型做督導式摘要抽取,包括支撐向量機~(Support Vector Machine)~、排序支撐向量機~(Rank Support Vector Machine)~、以及結構支撐向量機~(Structured Support Vector Machine)~,並和常用的非督導式最大邊際關聯法~(Maximum Margin Relevance)~比較。實驗顯示結構式支撐向量機可整體學習~(Joint Learn)~出原本需人為定義的參數,且優於其他督導式及非督導式的方法。此外,對各類特徵進行分析時,也發現韻律特徵對文件摘要有極大幫助,且各類特徵有加成性,串接後對文件摘要皆有幫助。

督導式摘要的最大問題在於人工標註摘要的取得不易,且不同語料的人工標註答案都是有差異~(Bias)~的。因此,本論文進一步研究領域調適之方法,利用其他領域的人工標註摘要資訊,迭代訓練結構式支撐向量機並利用特徵轉換減少領域間資料分佈的差異。在抽取課程錄音摘要的實驗中,透過利用新聞語料的人工標註答案和領域調適,發現可以得到相當大的進步。

最後,本論文對抽象式文件摘要提出了一套新架構並作了初步研究,有效利用詞與詞前後文的關係建圖~(Graph)~,圖中節點代表詞並在上面做搜尋產生候選語句,最後利用詞N連語言模型、POS N連語言模型以及主題模型對候選語句做重排序,對將來抽象式摘要的研究提供了良好的起點。

誌謝.......................................... i
中文摘要....................................... ii
一、導論....................................... 1
1.1 研究背景.................................. 1
1.2 本論文研究方向.............................. 2
1.3 本論文研究貢獻.............................. 3
1.4 章節安排.................................. 4
二、背景知識介紹.................................. 5
2.1 語音文件摘要背景介紹與相關研究 ................... 5
2.1.1 文件摘要之分類及應用...................... 5
2.1.2 文件摘要之方法.......................... 7
2.1.3 語音文件摘要與文字摘要之不同 ................ 10
2.1.4 傳統非督導式語音文件摘要方式-最大邊際關聯法(MMR) . . . 11
2.2 結構式支撐向量機............................. 12
2.2.1 機器學習.............................. 13
2.2.2 支撐向量機 ............................ 14
2.2.3 鑒別函數(DiscriminativeFunction) ............... 22
2.2.4 減損函數(LossFunction) ..................... 23
2.3 文件摘要評估方式............................. 24
2.3.1 準確率 (Precision) 、召回率 (Recall) 與F評估 (F-measure) . . 25
2.3.2 ROUGE-N ............................. 25
2.4 本章總結.................................. 26
三、利用結構支撐向量機及韻律特徵之抽取式語音文件摘要 . . . . . .27
3.1 簡介..................................... 27
3.2 督導式模型融入最大邊際關聯法..................... 27
3.3 結構式支撐向量機及目標函數之定義 .................. 29
3.4 抽取特徵.................................. 31
3.4.1 語意特徵.............................. 31
3.4.2 相似度特徵 ............................ 33
3.4.3 韻律特徵.............................. 34
3.4.4 其他特徵.............................. 38
3.5 實驗基礎設置 ............................... 39
3.5.1 實驗語料與辨識.......................... 39
3.5.2 參考摘要之形成.......................... 39
3.5.3 實驗配置.............................. 40
3.5.4 評估方式.............................. 40
3.6 實驗結果與分析 .............................. 40
3.6.1 基準實驗.............................. 40
3.6.2 初步特徵效力分析與串接 .................... 43
3.6.3 結構式支撐向量機法 ....................... 44
3.6.4 韻律特徵效力分析 ........................ 45
3.7 本章總結.................................. 46
四、使用結構支撐向量機作領域調適於跨語料之抽取式語音文件摘要 . . .47
4.1 簡介..................................... 47
4.2 架構與流程(Framework).......................... 48
4.3 領域調適.................................. 49
4.3.1 自我標註.............................. 49
4.3.2 特徵轉換.............................. 51
4.4 特徵抽取.................................. 53
4.4.1 領域獨有特徵 ........................... 53
4.4.2 共有特徵.............................. 54
4.5 實驗基礎設置 ............................... 56
4.5.1 實驗語料與辨識系統 ....................... 56
4.5.2 參考摘要之形成.......................... 56
4.5.3 實驗配置.............................. 57
4.5.4 評估方法.............................. 57
4.6 實驗結果與分析 .............................. 57
4.6.1 跨領域特徵效能分析 ....................... 57
4.6.2 自我標記以及領域獨有特徵之效能分析 ............ 60
4.6.3 特徵轉換.............................. 60
4.7 本章總結.................................. 63
五、以圖建構抽象式文件摘要之新架構...................... 64
5.1 簡介..................................... 64
5.2 架構與流程(Framework) ......................... 65
5.3 候選語句生成 ............................... 66
5.3.1 前處理 ............................... 66
5.3.2 建圖 ................................ 67
5.3.3 光束搜尋.............................. 68
5.4 抽象摘要生成 ............................... 69
5.4.1 訓練模型.............................. 69
5.4.2 候選語句評估 ........................... 71
5.5 實驗基礎設置 ............................... 72
5.5.1 使用語料.............................. 72
5.5.2 參數配置.............................. 72
5.5.3 實驗評估.............................. 73
5.6 實驗結果.................................. 73
5.7 小結..................................... 75
六、結論與展望 ................................... 76
6.1 結論..................................... 76
6.2 未來研究方向 ............................... 77
參考文獻....................................... 79

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