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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 盧信銘(Hsin-Min Lu) | |
dc.contributor.author | Yen-Hsiu Chen | en |
dc.contributor.author | 陳妍秀 | zh_TW |
dc.date.accessioned | 2021-06-17T04:38:11Z | - |
dc.date.available | 2023-08-09 | |
dc.date.copyright | 2018-08-09 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-07 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70779 | - |
dc.description.abstract | 近年來文字分析在財報中的應用相當廣泛,但是研究者感興趣的議題只有特定幾項,再加上財報項目擷取的品質好壞會影響後續分析的結果,因此本實驗提出以機器學習的模型來解決此任務。在第一階段進行財報的人工標記,蒐集訓練資料,以反映報表真實情況;第二階段針對訓練資料集設計六種不同的特徵,並運用條件隨機域讓模型自行根據學到的潛在規則進行文字序列標記。根據本實驗結果可以發現使用條件隨機域的方式進行全文的項目擷取,可以有效地提升擷取準確度,確保分析前的資料品質。而在這之中,項目標題文字對於標記的結果影響較大,項目編號和 item 此字較無任何影響。 | zh_TW |
dc.description.abstract | Textual Analysis is widely used in financial reports. However, there are only a few specific topics that researchers are interested in, and the quality of the item extraction will affect the results of the subsequent analysis. Therefore, in this research, we propose a machine learning model to extract an item from 10-K reports. First, to reflect the real situation of the reports, this study carries out manual tagging of the financial report and collects training materials. Second, we design six different features for the training dataset, and use the conditional random field to label text sequences based on the potential rules learned. According to the results of this experiment, it can be found that the use of conditional random fields for the full-text item extraction can effectively improve the accuracy of the extraction and ensure the quality of the data before analysis. Among them, the title text of the project has a great influence on the result of the mark, and the item number and the item have no influence. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T04:38:11Z (GMT). No. of bitstreams: 1 ntu-107-R05725037-1.pdf: 2081260 bytes, checksum: 7b477e7c898a7c1c29f96e19308b4fbd (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 口試委員審定書 i
誌謝 ii 摘要 iii Abstract iv 第一章緒論 1 1.1. 研究背景與動機 1 1.2. 研究目的 4 1.3. 研究架構 5 第二章文獻探討 6 2.1. 財報文字分析的介紹 6 2.1.1. 應用層面 7 2.1.2. 資料前處理層面 8 2.1.3. 小結 11 2.2. 文字序列資料的模型介紹 12 2.2.1. 統計模型 13 2.2.2. 類神經網路 15 2.2.3. 混合型 18 2.2.4. 小結 19 2.3. 條件隨機域的特徵設計 19 第三章、研究資料概觀與資料處理 21 3.1. 資料來源與相關介紹 21 3.2. 資料處理 23 3.2.1. 資料蒐集 23 3.2.2. 資料前處理 24 第四章研究方法 25 4.1. 研究流程 25 4.2. 標記規則(Labeling Scheme) 26 4.3. 人工標記流程 29 4.3.1. 標記流程 29 4.3.2. 檔案準備 29 4.3.3. 品質控管 31 4.3.4. 標記敘述統計 33 4.4. 特徵建構 34 4.4.1. 位置特徵 35 4.4.2. 句子首字特徵 35 4.4.3. 句子的單詞和雙詞特徵 36 4.4.4. 標題特徵 37 4.4.5 目錄反指標特徵 39 4.4.6. 特殊指向特徵 40 4.5. 模型訓練 41 4.5.1 CRF模型參數設定 41 4.5.2. Baseline設計 41 4.6. 模型評估 43 第五章實驗結果 45 5.1. 模型參數調整 45 5.2. 模型評估 46 5.3. 錯誤分析 49 5.3.1. Item 6、Item 8、Item 15、O混淆 49 5.3.2. 沒有標題和item的字眼 50 5.3.3. 內文指向其他區段 50 5.3.4. 報表內容難以判斷 51 5.3.5. 人工標記錯誤 53 第六章結論與建議 54 6.1. 實驗結論與建議 54 6.2. 研究貢獻 55 6.3. 未來研究方向 55 第七章參考文獻 56 附錄一 59 附錄二 60 | |
dc.language.iso | zh-TW | |
dc.title | 財報項目全文的擷取和效能評估 | zh_TW |
dc.title | Item Extraction for Annual Financial Report: Annotation and Evaluation | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 余峻瑜(Jiun-Yu Yu),洪為璽(Wei-Hsi Hung) | |
dc.subject.keyword | 人工標記,條件隨機域,項目擷取,序列標記,10-K 財報, | zh_TW |
dc.subject.keyword | Manual Tagging,CRF,Item Extraction,Sequence Labeling,10-K Report, | en |
dc.relation.page | 61 | |
dc.identifier.doi | 10.6342/NTU201802638 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-08-08 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
顯示於系所單位: | 資訊管理學系 |
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