本篇論文實現了主動式學習之古漢語斷詞，並實用於【明實錄】上，我們以主動式學習取代需要大量人力標註的監督式學習，並且改善非監督式學習需要透過資料量才能增加精準度的缺點，透過主動式學習的網頁呈現出可能錯誤的片段，減少標註人員修正的次數。 ;Currently, advanced Natural Language Processing (NLP) includes event extraction or event classification, automatic text summarization and so on. Most NLP techniques for classical Chinese are still on the early stage, like sentence segmentation or word segmentation, named entity recognition. These basic applications usually use supervised learning to identify. Tagging the training data of these basic applications need to spend much time, because the people that know the classical Chinese are minority. Therefore, the current advanced Natural Language Processing for classical Chinese are difficult to develop. The basic element of most languages is word. The accuracy of word segmentation influences the effect of the current advanced Natural Language Processing directly. As a result, we develop the word segment system for classical Chinese. Compared with traditional word segmentation, we do not need training data. This thesis focuses on applying active learning to word segmentation of historical texts. In addition, we apply the algorithm to the MING SHILU. We use active learning because it can reduce the annotation efforts significantly. We also mitigate the disadvantage of unsupervised model that needs large amounts of data to achieve satisfactory accuracy.
