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| Title: | 唐代墓誌銘與中國佛教寺廟志斷句研究
Sentence Segmentation for Tomb Biographies of Tang Dynasty and Chinese Buddhist Temple Gazetteers |
| Authors: | 張逸
Chang, Yi |
| Contributors: | 劉昭麟
Liu, Chao-Lin
張逸
Chang, Yi |
| Keywords: | 深度學習
機器學習
自然語言處理
Deep learning
Machine learning
Natural language processing |
| Date: | 2018 |
| Issue Date: | 2018-09-03 15:52:15 (UTC+8) |
| Abstract: | 20世紀以前，中文書寫並沒有使用標點符號的習慣，閱讀時必須憑個人經驗和語感對文章進行斷句理解。由於個人的經驗和習慣的不同，往往會對文章造成對不一樣的解讀甚至是誤解，因此，斷句是理解文章最基礎且困難的第一步驟。因此過去學者通過正規表示式、機器學習、深度學習等不同的方法作為自動化文言文斷句的方式，減少文史專家處理斷句的時間。
儘管目前已有許多自動斷句的研究，卻尚未出現一個系統將其整合並達到最佳的斷句效果。因此本研究設計一套實驗流程，將過去的研究成果進行組合測試，並觀察在不同組合測試下的Precision、Recall、F1等評估指標找出最佳的組合，進一步減少處理斷句的時間。
關於實驗流程的設計，以「唐代墓誌銘」以及「中國佛教寺廟志」作為實驗語料，並且使用「條件隨機場(Conditional Random Fields, CRF)」以及「Long Short-Term Memory(LSTM)」兩種在過去自動斷句研究中表現良好的模型與配合前後文特徵作為baseline，進行進一步的特徵與模型相關的組合實驗。特徵相關的實驗是藉由在baseline中加入各種不同的特徵找出有用的項目，而模型相關的實驗觀察不同機器學習方法與模型訓練方法建找出能夠增進模型效果的項目。
在本研究的實驗結果中，效果最好的特徵是前後文以及斷詞統計量，而效果最好的模型是整合了CRF與LSTM所產生的模型CRF+LSTM，其中CRF加入了弱點補強的演算法增強其效果，最後在唐代墓誌銘以及中國佛教寺廟志兩個語料中作為評估指標的F1值分別達到了0.873以及0.675。
Prior to the 20th century, using punctuation in articles hasn`t become a total phenomenon. Therefore readers have to comprehend passages through their personal experiences and the notion to the context, which caused challenges to decode articles accurately due to individual differences. Thus, the punctuation is a difficult first step towards the understanding of articles.
Although plenty research has been done, a fully optimized performance automatic punctuation system is still yet to come. In search of the best optimized combination of auto-punctuation system, this research designed an experiment protocol which testing various combination of evaluation index, e.g., Precision, Recall, F1 and previous research data.
The experiment protocol was using “Tomb Biographies of Tang Dynasty” and “Chinese Buddhist Temple Gazetteers” as text corpus, in which the Conditional Random Fields (CRF) and the Long Short-Term Memory (LSTM), favorited and well-performed models in the past research, was applied as a baseline for conducting further experiment of the combination of feature and model. For the feature related experiment was extracting valid entry via adding various item entry in baseline; the model related experiment was enhancing model performance by observing various machine learning and model training methods.
The results of the study shows that the best performed feature was the context and statistic of word segmentation. As for the best model was the combination of CRF and LSTM, the CRF+LSTM, in which the shortcoming of algorithm in CRF was patched as enhancement. As the result, the F1 score of both text corpuses: “Tomb Biographies of Tang Dynasty” and “Chinese Buddhist Temple Gazetteers” were reached 0.873 and 0.675. |
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| Description: | 碩士
國立政治大學
資訊科學系
104753032 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0104753032 |
| Data Type: | thesis |
| DOI: | 10.6814/THE.NCCU.CS.022.2018.B02 |
| Appears in Collections: | [資訊科學系] 學位論文
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