Please use this identifier to cite or link to this item:
https://ah.lib.nccu.edu.tw/handle/140.119/138003| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 蔡瑞煌<br>黃瀚萱 | zh_TW |
| dc.contributor.advisor | Tsaih, Rua-Huan<br>Huang, Hen-Hsen | en_US |
| dc.contributor.author | 薛卉吟 | zh_TW |
| dc.contributor.author | Xue, Hui-Yin | en_US |
| dc.creator | 薛卉吟 | zh_TW |
| dc.creator | Xue, Hui-Yin | en_US |
| dc.date | 2021 | en_US |
| dc.date.accessioned | 2021-12-01T06:30:04Z | - |
| dc.date.available | 2021-12-01T06:30:04Z | - |
| dc.date.issued | 2021-12-01T06:30:04Z | - |
| dc.identifier | G0108356036 | en_US |
| dc.identifier.uri | http://nccur.lib.nccu.edu.tw/handle/140.119/138003 | - |
| dc.description | 碩士 | zh_TW |
| dc.description | 國立政治大學 | zh_TW |
| dc.description | 資訊管理學系 | zh_TW |
| dc.description | 108356036 | zh_TW |
| dc.description.abstract | 奏摺，是研究清代政策實施和法制建設的珍貴的史料。雖然存於國立故宮博物院的清代宮中檔及軍機處的奏摺已完成數化，但應用仍然不普及，原因之一是辨識古典漢語的斷句、斷詞和詞義需花費歷史學家大量的時間。對於古典漢語，很少有有用的自然語言處理（NLP）工具，並且先進的人工智能（AI）模型學習不同朝代的訓練數據後，其性能也不盡相同。此外，沒有合適的NLP工具來分析清代的奏摺。為了解決有關於分析清代奏摺的挑戰，本研究探索一種基於Transformer之單任務學習（STL）及多任務學習（MTL）之模型，該模型可同時應付以下三個任務：斷句、斷詞、詞性（POS）標記和命名實體識別（NER）。為了完成此任務，本研究建議的標記方案包括三個部分：（1）用於斷句的BOE格式標籤；（2）用於斷詞的BIES格式標籤；以及（3）用於POS和NER的聯合標籤。為了評估該提案，本研究著重於雍正皇帝時期之奏摺，並收集並建立由中文專業人士參照新標籤標記方案所標註的清朝宮中檔奏摺數據集。研究結果顯示，斷句及斷詞任務中，多任務學習效能顯著優於單任務學習，兩個學習方法在詞性標記和命名實體識別則無顯著差異。模型的斷句結果可以達到輔助初學者們閱讀奏摺，斷詞以及詞性的標注結果則可以協助學者辨認詞義，減少對詞義誤讀的可能。 | zh_TW |
| dc.description.abstract | Memorials are important materials for research on policy implementation and the formation of legal institutions. Although the memorials of Qing palace and the Grand Council had been accomplished with image scanning, the application is still not popular in academia. One of the reasons is that classical Chinese will often take a lot of historian’s time to determine the segmentation of sentences and the meaning of words. The use of natural language processing (NLP) tools for analyzing classical Chinese remains an emerging topic in the digital humanity community. For classical Chinese, there are few NLP tools, and the performance of artificial intelligence (AI) models is not the same after learning the data of different dynasties. To address the challenges regarding the memorials of Qing dynasty, this study proposes a classical Chinese analysis model with transformer-based single task learning (STL) and multitask learning (MTL) that simultaneously copes with three tasks for classical Chinese: word segmentation, sentence segmentation, and the joint task for part-of-speech (POS) tagging and named entity recognition (NER). To accomplish the goal, the labels have three parts: (1) BOE format tags for sentence segmentation, (2) BIES format tags for word segmentation, and (3) the joint tags for POS and NER. For evaluating the proposal, this study focuses on the Yong-zheng (雍正) emperor and the Qing’s memorials dataset annotated with new tagging schemes by Chinese professionals is collected. The research results show that method MTL performs significantly better on both sentence segmentation task and word segmentation task than method STL. And on POS+NER task, there is no significant difference between the two methods. The prediction of the memorials can help scholars to read memorials easily and reduce the probability of misinterpretation of word meaning. | en_US |
| dc.description.tableofcontents | 1 INTRODUCTION 7\n2 PREVIOUS WORKS 9\n2.1 Qing Palace Memorials of National Palace Museum 9\n2.2 Chinese Text Classification Tasks 10\n2.3 Bidirectional Encoder Representations from Transformers 12\n2.4 RNN-based Multi-Task Learning 14\n2.5 Bidirectional Gate Recurrent Unit 15\n3 EXPERIMENT DESIGN 17\n3.1 Models 17\n3.2 Input X 20\n3.3 Output Tags 20\n3.3.1 Sentence Segmentation Tags 20\n3.3.2 Word Segmentation Tags 21\n3.3.3 Joint Tags of POS and NER 21\n3.3.4 Example 23\n3.4 Dataset 24\n3.4.1 Data Collection for Qing’s Dataset 24\n3.4.2 Data Labeling for the Qing’s Dataset 26\n3.4.3 Statistical Description of the Qing’s Dataset 27\n3.5 Experiment Environment 28\n3.7 Evaluation 29\n4 EXPERIMENTS 30\n4.1 Preprocessing 30\n4.2 Training 30\n4.3 Evaluation 31\n4.4 Comparisons 34\n4.4.1 Residual Connection 34\n4.4.2 Compare with Other Models 34\n4.4.3 Compare with Other Chinese NLP Tools 35\n4.4.4 Different Tagging Scheme of POS+NER 35\n4.4.5 Different Granularity of Word Segmentation 36\n4.5 Discussion 37\n5 CONCLUSION 41\nREFERANCE 43\nAPPENDIX 46\nChinese Version of Interview and Feedback 46 | zh_TW |
| dc.format.extent | 1466792 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri | http://thesis.lib.nccu.edu.tw/record/#G0108356036 | en_US |
| dc.subject | 清代奏摺 | zh_TW |
| dc.subject | 斷詞斷句 | zh_TW |
| dc.subject | 命名實體識別 | zh_TW |
| dc.subject | 多任務學習 | zh_TW |
| dc.subject | 自然語言處理 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 古文 | zh_TW |
| dc.subject | Memorial | en_US |
| dc.subject | Qing Dynasty | en_US |
| dc.subject | Transformer | en_US |
| dc.subject | BERT | en_US |
| dc.subject | Sentence segmentation | en_US |
| dc.subject | Word segmentation | en_US |
| dc.subject | Name entity recognition | en_US |
| dc.subject | Multitask learning | en_US |
| dc.subject | Classical Chinese | en_US |
| dc.subject | NLP | en_US |
| dc.title | 基於Transformer之多任務學習用於清代奏摺斷句斷詞命名實體識別 | zh_TW |
| dc.title | Text Segmentation and Name Entity Recognition for Memorials from the Qing Dynasty with Transformer-based Multitask Learning | en_US |
| dc.type | thesis | en_US |
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| dc.identifier.doi | 10.6814/NCCU202101726 | en_US |
| item.openairetype | thesis | - |
| item.fulltext | With Fulltext | - |
| item.cerifentitytype | Publications | - |
| item.grantfulltext | open | - |
| item.openairecristype | http://purl.org/coar/resource_type/c_46ec | - |
| Appears in Collections: | 學位論文 | |
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