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研究生: 陳信嘉
Shin-Chia Chen
論文名稱: 具自動新聞摘要處理之智慧型行動位置新聞資訊服務系統
Intelligent Location-based Mobile News Service System with Automatic News Summarization
指導教授: 洪欽銘
Hong, Chin-Ming
陳志銘
Chen, Chih-Ming
學位類別: 碩士
Master
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2007
畢業學年度: 95
語文別: 中文
論文頁數: 72
中文關鍵詞: 多文件摘要位置偵測行動裝置位置資訊服務
英文關鍵詞: Multi-Document Summariztion, location awareness, mobile device, Location-Based Service
論文種類: 學術論文
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  • 在無線網路環境的蓬勃發展及行動裝置軟硬體技術精進下,無線行動資訊服務之相關應用也勢必如雨後春筍般出現,帶給人們更便利的生活。在行動資訊領域中,日愈重要的應用服務-『位置資訊服務系統』(Location-Based Service System)是透過偵測使用者的所在位置及結合相關位置資訊,即時提供使用者所需資訊的一種服務系統,以協助使用者取得位置相關有用資訊。
    然而,一個位置資訊服務系統開發,除了要先具備基本的服務與資料傳輸架構外,還須具備以下兩個要素:1.服務的資訊類型,關於生活上的資訊不外乎氣象、旅遊、新聞…等。雖然氣象與旅遊資訊可由特定的網站直接取得,但是要取得多文件的新聞摘要,則須另外處理。為此,本研究提出了一個基於模糊理論(Fuzzy Theorm)的多文件摘要(Multi-Document Summariztion)方法來產生同事件的多個文件的新聞摘要;2.用戶端的位置偵測(Location Awareness),當系統了解用戶端的位置後,才能提供位置相關的資訊。故本研究還提出了一個新的位置偵測分類器,藉由結合GPS(Golbal Positioning System)之經緯度座標信號來自動偵測用戶端地理位置。
    最後,本研究整合以上所提出的方法,以發展出「具自動新聞摘要處理之智慧型行動資訊服務系統」,來提供基於使用者地點的整合性資訊服務。根據實驗分別對文件摘要方法與位置偵測分類器做評估,在参數最佳化時,可產生81%良好與15%可接受的摘要品質,位置偵測分類器亦可達90%的正確率。因此,證明本研究提出的「具自動新聞摘要處理之智慧型行動資訊服務系統」具有實際應用的價值。

    Due to fast development of wireless network and mobile computing technologies, mobile information services make more convenient in terms of getting useful information in our dalily life. Mobile information service will become more and more important in the future. “Localcation-Based Service (LSB) System” in mobile service can sense a user’s position and integrate location information to individual users, thus helping users obtain the useful information. However, developing a Location-Based Service System must be based on information service and deliver structure with additional two elements: one is information type provider in server-side and another is location awareness in client-side. This study mainly focused on developing a Location-Based Service System which can provide location-based news with news summarization service to individual users. Therefore, this study proposed a novel multi-document summarization method based on fuzzy theorm to provie summarized news to mobile devices. Moreover, to sense user location precisely, the study also proposed a practicable location awareness classifier by aware GPS (Golbal Positioning System) signals to identify user’s position. Finally, the study itegrated the proposed multi-document summarization method and user location identification scheme to construct “Intelligent Location-based Mobile News Service System with Automatic News Summarization” in order to provide users about location information services. According to experimental results in Mutli-Document Summarization and user location identification, news summarization with good quality is up to 86%, the accuray rate of user location awareness classifier is up to 90%. Therefore, these experimental results can prove that the proposed “Intelligent Location-based Mobile News Service System with Automatic News Summarization” can be successfully applied for location-based services in real-world application.

    目錄 中文摘要 I 英文摘要 II 誌謝 III 目錄 IV 圖目錄 VI 表目錄 VIII 第一章 緒論 1 1.1 研究動機與目的 1 1.2 研究方法 3 1.3 研究架構 3 第二章 相關文獻與相關理論探討 4 2.1位置服務系統 4 2.2文件摘要相關研究 6 2.2.1 自動化文件摘要技術的發展 7 2.2.2 自動化文件摘要技術相關研究工作 8 2.2.3 以文件集為基礎的摘要技術 10 2.2.4 以主題關係地圖(Text Relationship Map)為基礎的摘要技術 13 2.2.5 以語段模型(Discourse Model)為基礎的摘要技術 17 2.2.6 其他基礎之摘要技術 18 2.3 相關理論背景知識 22 2.3.1 中文斷詞處理 22 2.3.2 字詞之權重計算 23 2.3.3 向量空間模型 25 2.3.4 詞彙-文件矩陣 26 2.3.5 相似度計算 27 第三章 研究方法與系統架構 28 3.1 當地新聞摘要與資訊派送系統架構 28 3.2 網頁資訊擷取代理人(Crawler Agent) 30 3.3 新聞摘要處理代理人 31 3.3.1 處理新聞摘要流程 32 3.3.2 斷詞處理 34 3.3.3 摘要產生之流程 36 3.4 定位代理人 43 3.4.1地理資訊位置偵測法 43 3.4.2以機器學習為基礎的位置偵測法 44 第四章 實驗與分析 50 4.1 多文件摘要實驗結果分析與評估 50 4.2 各種演算法於位置偵測之比較 58 4.3 本系統之行動裝置使用者介面簡介 67 第五章 結論與未來發展 69 5.1 結論 69 5.2 未來發展 70 參考文獻 72 圖目錄 圖2-1 Paragraph Relationship Map 與其對應的Text Segmentation 15 圖2-2 計算Aggregate Similarity 的概念圖示 16 圖2-3 Vector representation of document space 25 圖2-4 詞彙-文件矩陣 26 圖3-1 位置服務系統架構Server 端 29 圖3-2 位置服務系統架構Client 端 30 圖3-3 HTML網頁原始碼 31 圖3-4 Google News 文件分類架構 33 圖3-5 新聞摘要處理流程 34 圖3-6 ECScanner斷詞系統 35 圖3-7 長度分數計算 37 圖3-8 地理資訊資料結構 44 圖3-9 台北市各區之經緯度座標中心點 46 圖3-10 以Average-KNN演算法對未知類別樣本的歸類 49 圖4-1 縣市邊緣 58 圖4-2 非縣市邊緣 58 圖4-3 類神經網路訊練樣本之隱藏層與MSE 62 圖4-4 類神經網路對縣市判斷 62 圖4-5 Naïve Bayesian Classifier 的縣市偵測 63 圖4-6 KNN與Average-KNN縣市邊緣偵測的正確率統計 64 圖4-7 KNN與Average-KNN整體縣市偵測的正確率統計 65 圖4-8 各類型分類器之整體縣市偵測的正確率 66 圖4-9 使用者所在的縣市 67 圖4-10 系統提供之資訊項目 67 圖4-11 當地的新聞標題 68 圖4-12 當地新聞與摘要評選 68 表目錄 表2-1 Local weight 24 表2-2 Global weight 24 表2-3 常見的相似度公式 27 表3-1 段落相似的關係表 38 表3-2 嘉義縣市樣本點 47 表4-1 實驗1.1專家評定摘要的結果 52 表4-2 實驗1.2專家評定摘要的結果 52 表4-3 11組權重向量之摘要品質統計 54 表4-4 摘要品質為Ok and Good出現在文件中的位置統計 54 表4-5 摘要範例 55 表4-6 短字數段落新聞文件範例 56 表4-7 長字數段落新聞文件範例 57 表4-8 縣市邊緣之測試樣本點 59 表4-9 非縣市邊緣判斷之測試樣本點 60 表4-10 經緯度的名義變數劃分 61 表4-11 SVM於縣市邊緣偵測 63 表4-12 SVM於非縣市邊緣偵測 64

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