臺灣博碩士論文加值系統

隨著網際網路上多國語言文件的增加，多國語言資訊檢索技術的應用成為一個重要的研究課題。本文描述我們在發掘多種語言文件上的知識所發展的一個方法。我們從光華雜誌中收集中文與英文的新聞資料，測試語料庫中各有976份中英雙語文件。
在本研究中，我們採用一類神經網路中文件分群的方法，即增長層級式自我組織映射圖，來協助我們發現多國語言文件之關聯。我們使用中英雙語平行語料庫來建構實驗以發掘文件間之關連性。本研究實驗顯示我們的方法可以獲取不同語言文件間之關係。

With the increasing amount of multilingual texts in the Internet, multilingual information retrieval has become an important research issue. This paper describes our work on developing a method for discovery of knowledge from multilingual documents. We collected English and Chinese news articles from the Taiwan-panorama magazine. Our test corpus includes 976 pairs of Chinese-English parallel documents.
In this study, we adopt a text clustering approach, which apply a neural network approach, namely the growing hierarchical self-organizing maps (GHSOM), to help us discovering relationships among multilingual documents. We have conducted experiments to uncover relationships of documents based on Chinese-English bilingual parallel corpora. The experimental results show that our multilingual text mining approach may capture conceptual relationships among documents written in different languages.

致謝....................................I
中文摘要................................II
Abstract................................III
目錄....................................IV
表目錄..................................V
圖目錄..................................VI
第一章 緒論.............................1
1.1 研究背景............................1
1.2 研究動機............................2
1.3 問題領域............................4
1.4 論文架構............................4
第二章 相關研究探討.....................5
2.1 多國語言資訊檢索....................5
2.1.1 辭典為本方法......................5
2.1.2 索引典為本方法....................7
2.1.3 語料庫為本方法....................8
2.2 文件分群演算法......................12
2.2.1 k-Nearest Neighbor Classifier.....12
2.2.2 Na??ve Bayesian Classifier........13
2.2.3 Neural Network Classifier.........13
第三章 研究方法與實驗設計...............16
3.1 文件前置處理........................17
3.1.1 斷詞..............................17
3.1.2 特徵選取..........................18
3.1.3 向量空間模型......................21
3.2 文件分群............................22
3.2.1 自我組織映射圖(SOM)...............22
3.2.2 增長層級式自我組織映射圖(GHSOM)...25
第四章 實驗結果.........................30
4.1 實驗步驟............................30
4.1.1 前置處理..........................31
4.1.2 文件分群..........................34
4.1.3 檢索介面..........................37
4.2 實驗評估............................43
第五章 結論與未來研究方向...............49
5.1 章節回顧............................49
5.2 結論................................50
5.3 未來研究方向........................50
參考文獻................................52

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