Difangzhi is the local gazetteers compiled by local government of China. Its content is plenty and extensive. It’s including many undetected information, like biographical information, geographical information, and officer record information and so on. Because of the difference between Difangzhi corpus and modern Chinese language, we should not use current natural language processing tools directly. In order to extract biographical information, we construct our model to recognize the named entity and use the noun list to assist our annotation method in Difangzhi corpus.
In this study, we use supervised learning to construct our model. At first, we need to generate our training data. According to the personal information list with manual verification and noun lists, we have reliable information to annotate words in Difangzhi corpus. However, they still have some noise in those lists. As a result, we must do the preprocessing to those lists for cleaning. After, the ambiguity problem will happen when we trying to annotate our corpus. Here we provide three methods to annotate our corpus with disambiguation. Using the annotated corpus to generate training data and built the condition random fields models. In our experiment, we use our models generated by three different annotate methods to predict the character label in testing Difangzhi corpus. According to the labeled result, we extract the person name and address name to evaluate. The result shows the precision of person name recognition is over 80%, and precision of address name recognition is about 86%. Because of the training corpus and test corpus is quite similar, the performances of our model is pretty well. Therefore, we use labeled result to find correlation of person name and address name. Using a simple way to connect person name and address name and sampling the result to evaluate. The sample result shows we could connect person name and address name correctly in some specific grammars. In order to analyze more deeply, we attempt to split clauses in Difangzhi corpus. Use finite state machine model to recognize the beginning of clauses. Although the result shows we could find some beginning of clauses, but our method still lose many beginning of clauses.
In the future work, we attempt to add more information to annotate Difangzhi corpus and modify our disambiguated methods to make the recognition result better. In order to get more information about the person in the corpus, we will try to split paragraphs or sentences more precisely. Besides, we also try to analyze grammar in the corpus. Finding useful pattern to connect person name and other entities, like address name, officer name and so on. Generating the information about people appears in the corpus automatically.

第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究方法 2
1.3 主要貢獻 3
1.4 論文架構 3
第二章 文獻探討 4
2.1 數位人文之相關研究 4
2.2 實體名詞辨識之相關研究 5
第三章 地方志文本 7
3.1 地方志書影 7
3.2 地方志數位化 8
3.3 地方志史料數量與問題定義 9
第四章 系統架構 11
4.1 系統流程 11
4.2 實體名詞辨識模型訓練工具 13
第五章 產生標記語料 14
5.1 IOB format 結合 BIES標記類別 14
5.2 資料前處理 16
5.2.1 刪除地方志未用標記 16
5.2.2 卷分割地方志語料 16
5.2.3 異體字補充 16
5.2.4 原始名詞表內容比對及分析 18
5.2.5 標記規則 19
5.3 解歧標記方法 21
5.3.1 順序標記法 21
5.3.2 列舉式標記法 23
5.3.3 列舉式標記法加入朝代資訊 27
第六章 建立實體名詞辨識模型 31
6.1 隨機條件場 31
6.2 特徵擷取 33
第七章 實驗結果與分析 43
7.1 評估實體名詞辨識模型實驗 43
7.1.1 人名擷取實驗 43
7.1.2 地名擷取實驗 47
7.2 辨識結果應用之實驗 50
7.2.1 人名與地名連結 50
7.2.2 人物條目切割 55
第八章 結論與未來展望 59
7.1 結論 59
7.2 未來展望 60
參考文獻 62
附錄I 論文口試相關討論 65

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