跨語言文章鏈結是一項從百科全書中找尋不同語言之對等的文章配對，這項研究的困難在於如何從數個有著相似標題或者內容的候選文章中釐清哪著才是正確的對應文章。現有的研究方法大多數均採特徵工程的方法，專注於發展基於文字或者文章連結的特徵，但是這樣的方法通常都很費時而且有些特徵只能在相同的百科全書中運用。在這篇論文中，我們提出跨百科命名實體嵌入模以解決上述提到的問題。不同於先前的研究，我們提出的方法並不需依賴已知的跨語言文章連結，並且我們將提出的方法運用於英文維基百科與中文百度百科之煎的跨語言文章鏈結上。我們的所設計的特徵使效能相較於基線方法提升了29.62\%，而目前最佳系統相較於基線方法僅有26.86\%的效能提升，測試30次不同的實驗結果顯示，我們的系統相較於目前最佳系統有2.76\%的平均效能提升，經由統計檢驗得到我們的方法和目前最佳方法有顯著差異。

Cross-language article linking (CLAL) is the task of finding corresponding article pairs of different languages across encyclopedias. This task is a difficult disambiguation problem that one article must be selected among several candidate articles with similar titles and contents. Existing works focus on engineering text-based or link-based features for this task, which is time-consuming job and some are only applicable within the same encyclopedia. In this paper, we address these problems by proposing cross-encyclopedia entity embedding. Unlike other works, the proposed method does not rely on known cross-language pairs. We apply the proposed method to CLAL between English Wikipedia and Chinese Baidu Baike. Our features improve performance relative to the baseline by 29.62\%. Tested 30 times, our system achieved an average improvement of 2.76\% over the current best system (26.86\% over baseline), a statistically significant result.

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