臺灣博碩士論文加值系統

本研究探討雙語國際新聞的對應，新聞文章的分群以及後續新聞的事件歸類。在雙語新聞對應時，我們依詞彙在標題與內文出現的差異給予不同的權重，並使用語料庫進行詞彙之間的相似度計算找出相對應的新聞。在新聞分群方面，我們修改搜尋引擎結果分群方法，利用潛在語意分析找出文件之間的相關詞，並依語意分析的概念維度分數進行相關詞的分群，並透過新聞文章所包含的相關詞進行文件分群。接著依照各群集的代表詞彙做為虛擬類別的特徵詞訓練分類模型，進行後續新進文章的事件歸類。最後在對延伸閱讀部分主要分為命名實體連結以及新文主題語意關聯圖，讓讀者在讀完文章後能有進一步的知識補充。在文章對應，分群與後續文章的歸類皆有不錯的表現，但仍有許多改善空間。

This study conducts matching, clustering and topic categorization of bilingual international news. The matching of bilingual news extends previous work on bilingual international news matching and named entity linking. We assign different weights to terms depending on its appearance in document title or content and use bilingual corpuses to calculate the similarity between terms. We modify a Latent Semantic Analysis based clustering method for grouping news. The clustering process first finds the relevant terms associated with a news set, and then using semantic dimensionality scores to group the terms into clusters and in turn group the documents containing similar terms into news clusters. Finally, we consider the label terms of each news cluster as features of a virtual class to run training of a classifier for categorizing the up-coming news. The further reading contains entity linking and topic relation graph. The experimental results look promising; however, much room remains for improvement.

書名頁 i
論文口試委員審定書 ii
授權書 iii
中文摘要 vi
英文摘要 vii
誌謝 viii
目錄 ix
表目錄 xi
圖目錄 xii
第一章、 動機與目的 1
1.1 研究動機 1
1.2 研究目的 1
1.3 研究架構 2
第二章、 文獻探討 2
2.1 文件對應 2
2.2 新聞分群 5
2.3 新聞事件歸類 10
2.4 延伸閱讀 10
第三章、 研究方法 13
3.1 系統架構 13
3.2 雙語新聞對應 13
3.3 中文新聞分群 14
3.4 新聞事件歸類 16
3.4 延伸閱讀 18
第四章、 系統評估 23
4.1 實驗資料來源 23
4.2 雙語新聞對應 23
4.3 新聞分群 25
4.4 新聞事件歸類 27
4.5 延伸閱讀-命名實體辨識與對應 29
4.6 新聞主題語意關聯圖與雙語新聞網站呈現 31
第五章、 結論 33
5.1 結論 33
5.2 未來展望 34
第六章、 參考文獻 36

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