臺灣博碩士論文加值系統

新聞事件可描述為「一個時間區間內、同一主題的相似新聞之集合」，而新聞大多僅是一完整事件的零碎片段，其內容也易受到媒體立場或撰寫角度不同有所差異；除此之外，龐大的新聞量亦使得想要瞭解事件全貌的困難度大增。因此，本研究將利用文字探勘技術群聚相關新聞為事件，以增進新聞所帶來的價值。
分類分群為文字探勘中很常見的步驟，亦是本研究將新聞群聚成事件所運用到的主要方法。最近鄰 (k-nearest neighbor, kNN)搜尋法可視為分類法中最常見的演算法之一，但由於kNN在分類上必須要每篇新聞兩兩比較並排序才得以選出最近鄰，這也產生了kNN在實作上的效能瓶頸。本研究提出了一個「建立距離參考基準點」的方法RTD-based kNN (Relative Text-Distance-based kNN)，透過在向量空間中建立一個基準點，讓所有文件利用與基準點的相對距離建立起遠近的關係，使得在選取前k個最近鄰之前，直接以相對關係篩選出較可能的候選文件，進而選出前k個最近鄰，透過相對距離的概念減少比較次數以改善效率。
本研究於Google News中抽取62個事件(共742篇新聞)，並依其分群結果作為測試與評估依據，以比較RTD-based kNN與kNN新聞事件分群時的績效。實驗結果呈現出RTD-based kNN的基準點以常用字字彙建立較佳，分群後的再合併則有助於改善結果，而在RTD-based kNN與kNN的F-measure並無顯著差距(α=0.05)的情況下，RTD-based kNN的運算時間低於kNN達28.13%。顯示RTD-based kNN能提供新聞事件分群時一個更好的方法。最後，本研究提供一些未來研究之方向。

News Events can be described as "the aggregation of many similar news that describe the particular incident within a specific timeframe". Most of news article portraits only a part of a passage, and many of the content are bias because of different media standpoint or different viewpoint of reporters; in addition, the massive news source increases complexity of the incident. Therefore, this research paper employs Text Mining Technique to cluster similar news to a events that can value added a news contributed.
Classification and Clustering technique is a frequently used in Text Mining, and K-nearest neighbor(kNN) is one of most common algorithms apply in classification. However, kNN requires massive comparison on each individual article, and it becomes the performance bottlenecks of kNN. This research proposed Relative Text-Distance-based kNN(RTD-based kNN), the core concept of this method is establish a Base, a distance reference point, through a Vector Space, all documents can create the distance relationship through the relative distance between itself and base. Through the concept of relative distance, it can decrease the number of comparison and improve the efficiency.
This research chooses a sample of 62 events (with total of 742 news articles) from Google News for the test and evaluation. Under the condition of RTD-based kNN and kNN with a no significant difference in F-measure (α=0.05), RTD-based kNN out perform kNN in time decreased by 28.13%. This confirms RTD-based kNN is a better method in clustering news event. At last, this research provides some of the research aspect for the future.

第一章 緒論 1
第一節 研究背景 1
第二節 研究動機 2
第三節 研究目的 3
第二章 文獻探討 4
第一節 資料探勘 4
2.1.1 資料探勘定義 4
2.1.2 常用資料探勘方法 5
第二節 文字探勘 7
2.2.1 文字探勘定義 7
2.2.2 斷詞處理與權重計算 7
2.2.3 向量空間模型(Vector Space Model, VSM)的運用 11
2.2.4 相似度計算 12
2.2.5 分類技術 13
2.2.6 分群技術 14
第三節 k-最鄰近演算法 (k-Nearest Neighbor, kNN) 14
2.3.1 kNN分類演算法於文字探勘 14
2.3.2 kNN運用於新聞事件的偵測與追蹤 15
第三章 研究方法與設計 18
第一節 研究設計 18
第二節 RTD-based kNN 演算法 20
3.2.1 kNN分類法描述 20
3.2.2 kNN問題 22
3.2.3 參考距離的概念 22
第三節 分群結果的合併 24
第四節 新聞的偵測與追蹤 24
第五節 實驗流程與內容 26
第六節 評估方法 27
第七節 新聞來源與特性 28
第四章 實驗結果 29
第一節 基準點建立 29
第二節 事件偵測門檻值 33
第三節 文件相似門檻值 38
第四節 k值的提升 43
第五節 合併前後的差別 44
第六節 與kNN的比較 46
第五章 結論與未來展望 54
第一節 結論與建議 54
第二節 未來展望 56
參考文獻 57
附錄A：Google News新聞來源與事件 62
附錄B：RTD-based kNN群聚事件結果 63

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