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題名 以自然語言處理建構基於語意網的金融資產分類問答系統：以IFRS 9為基礎準則
Using Natural Language Processing and Semantic Web to Construct a Classification System for Financial Assets: an Example of IFRS 9作者 李長瑋
Li, Chang-Wei貢獻者 張祐慈<br>周濟群
Chang, Yu-Tzu<br>Chou, Chi-Chun
李長瑋
Li, Chang-Wei關鍵詞 語意網
自然語言處理
問答系統
金融資產分類
國際財務報導準則第9號
Semantic Web
Natural Language Processing
Question Answering
Classification of Financial Assets
IFRS 9日期 2022 上傳時間 1-Aug-2022 17:05:14 (UTC+8) 摘要 本研究目的在探討以少量樣本(訓練樣本總字數266字)但具備知識內涵的會計準則，如IFRS 9，是否可用於知識塑模，建立對應的語意網模型，並以此為基礎建立問答系統。本研究之方法包含兩大部分，第一部分是以AI技術，自然語言處理的成分句法分析(constituent parsing)，解構IFRS 9或問題(會計題目)中詞彙或片語，並考量其詞性，將詞彙或片語標記為Predicate (述詞)或Object (受詞)，此即為IFRS 9或問題之特徵，並使用非AI技術，語意網進行儲存。此外，本研究為捕捉更多特徵，將IFRS 9的特徵以金融領域的WordNet同義詞作為概念詞袋(bag-of-concepts)。第二部分則是語意重要性分析，負責將IFRS 9與問題的語意特徵比對，並以「語意重要性分數」來分析問題與IFRS 9中四種會計衡量方法的語意相似性，並得出金融資產問題中，應採用的會計衡量方法。本研究在語意相似度的比對上，提出「語意重要性分數」，其考慮在語意上是否有相同的特徵(Predicate或Object)，並考量特徵在特定會計衡量方法中是否具重要性。研究結果發現，輸入共計40道IFRS的教科書題目(總字數2,787字，平均135字)，分類系統在識別金融資產應採用的會計衡量方法正確率為92.50%，F1-score為94.60%，證明即使樣本數量不多，但樣本具有知識內涵亦可建構可使用的問答系統。本研究貢獻有三：一是提出轉換會計原則為語意網模型之方法及流程；二是本研究提出的「語意重要性分數」，此語意相似性的衡量有助於知識模型在問答系統中使用；三是驗證具知識內涵的會計準則不須大量樣本及標記，即可建構問答系統。
The purpose of my research is to design a question answering system using the knowledge modeling and the Semantic Web technology. I develop the question answering system in the context of IFRS 9, and test the accuracy of the system using questions selected from accounting textbooks. First, I use the constituent parsing of the natural language processing (NLP) tools to analyze words, phrases, and part of speech of the content of accounting standards and textbook questions. The results of the constituent parsing generate characteristics, including predicates or objects used in the accounting standards and textbook questions. Then I adopt Semantic Web tools to store the characteristics generated from the NLP analysis. To enhance the effectiveness of discovering characteristics, I further use the WordNet synonyms from the financial domain as a bag-of-concepts for the accounting context. Secondly, I perform semantic analysis and calculate semantic materiality scores. I compare the similarity of the semantics from IFRS 9 measurement and textbook questions. Finally, I conduct an experiment to classify the textbook questions and match them to the appropriate measurement method. My sample size comprises 40 questions retrieved from an accounting textbook. The output performance of the experiment shows the question answering system reaches a 92.50% accuracy rate and a 96.40% F1-score in classifying financial assets to the proper category. This study has three contributions: (1) I propose a joint method of using NLP and Semantic Web for a question answering system in the context of IFRS 9; (2) I develop the semantic materiality score to measure the similarity of semantics of accounting knowledge and apply it to the question answering system; (3) I provide evidence of the usefulness of the small sample size and labels for building a domain-specific question answering system.參考文獻 Aas, K., Jullum, M., & Løland, A. (2021). 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國立政治大學
會計學系
105353504資料來源 http://thesis.lib.nccu.edu.tw/record/#G0105353504 資料類型 thesis dc.contributor.advisor 張祐慈<br>周濟群 zh_TW dc.contributor.advisor Chang, Yu-Tzu<br>Chou, Chi-Chun en_US dc.contributor.author (Authors) 李長瑋 zh_TW dc.contributor.author (Authors) Li, Chang-Wei en_US dc.creator (作者) 李長瑋 zh_TW dc.creator (作者) Li, Chang-Wei en_US dc.date (日期) 2022 en_US dc.date.accessioned 1-Aug-2022 17:05:14 (UTC+8) - dc.date.available 1-Aug-2022 17:05:14 (UTC+8) - dc.date.issued (上傳時間) 1-Aug-2022 17:05:14 (UTC+8) - dc.identifier (Other Identifiers) G0105353504 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/140974 - dc.description (描述) 博士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 會計學系 zh_TW dc.description (描述) 105353504 zh_TW dc.description.abstract (摘要) 本研究目的在探討以少量樣本(訓練樣本總字數266字)但具備知識內涵的會計準則，如IFRS 9，是否可用於知識塑模，建立對應的語意網模型，並以此為基礎建立問答系統。本研究之方法包含兩大部分，第一部分是以AI技術，自然語言處理的成分句法分析(constituent parsing)，解構IFRS 9或問題(會計題目)中詞彙或片語，並考量其詞性，將詞彙或片語標記為Predicate (述詞)或Object (受詞)，此即為IFRS 9或問題之特徵，並使用非AI技術，語意網進行儲存。此外，本研究為捕捉更多特徵，將IFRS 9的特徵以金融領域的WordNet同義詞作為概念詞袋(bag-of-concepts)。第二部分則是語意重要性分析，負責將IFRS 9與問題的語意特徵比對，並以「語意重要性分數」來分析問題與IFRS 9中四種會計衡量方法的語意相似性，並得出金融資產問題中，應採用的會計衡量方法。本研究在語意相似度的比對上，提出「語意重要性分數」，其考慮在語意上是否有相同的特徵(Predicate或Object)，並考量特徵在特定會計衡量方法中是否具重要性。研究結果發現，輸入共計40道IFRS的教科書題目(總字數2,787字，平均135字)，分類系統在識別金融資產應採用的會計衡量方法正確率為92.50%，F1-score為94.60%，證明即使樣本數量不多，但樣本具有知識內涵亦可建構可使用的問答系統。本研究貢獻有三：一是提出轉換會計原則為語意網模型之方法及流程；二是本研究提出的「語意重要性分數」，此語意相似性的衡量有助於知識模型在問答系統中使用；三是驗證具知識內涵的會計準則不須大量樣本及標記，即可建構問答系統。 zh_TW dc.description.abstract (摘要) The purpose of my research is to design a question answering system using the knowledge modeling and the Semantic Web technology. I develop the question answering system in the context of IFRS 9, and test the accuracy of the system using questions selected from accounting textbooks. First, I use the constituent parsing of the natural language processing (NLP) tools to analyze words, phrases, and part of speech of the content of accounting standards and textbook questions. The results of the constituent parsing generate characteristics, including predicates or objects used in the accounting standards and textbook questions. Then I adopt Semantic Web tools to store the characteristics generated from the NLP analysis. To enhance the effectiveness of discovering characteristics, I further use the WordNet synonyms from the financial domain as a bag-of-concepts for the accounting context. Secondly, I perform semantic analysis and calculate semantic materiality scores. I compare the similarity of the semantics from IFRS 9 measurement and textbook questions. Finally, I conduct an experiment to classify the textbook questions and match them to the appropriate measurement method. My sample size comprises 40 questions retrieved from an accounting textbook. The output performance of the experiment shows the question answering system reaches a 92.50% accuracy rate and a 96.40% F1-score in classifying financial assets to the proper category. This study has three contributions: (1) I propose a joint method of using NLP and Semantic Web for a question answering system in the context of IFRS 9; (2) I develop the semantic materiality score to measure the similarity of semantics of accounting knowledge and apply it to the question answering system; (3) I provide evidence of the usefulness of the small sample size and labels for building a domain-specific question answering system. en_US dc.description.tableofcontents 第一章 緒論 ....................................... 1第一節 研究背景與動機 ................... 1第二節 研究目的 ......................... 3第三節 研究問題 ......................... 4第四節 研究限制 ......................... 4第五節 研究貢獻 ......................... 5第二章 文獻探討 ................................... 6第一節 人工智慧 ......................... 6第二節 語意網技術 ....................... 15第三節 語意網技術與自然言處理 ........... 22第三章 研究方法 ................................... 24第一節 分類系統架構及流程 ............... 24第二節 研究樣本 ......................... 25第三節 三元組命名空間 ................... 27第四節 準則特徵子系統 ................... 30第五節 問題特徵子系統 ................... 36第六節 語意比對子系統 ................... 40第七節 分類系統效能評估 ................. 54第四章 研究結果 ................................... 56第一節 樣本統計 ......................... 56第二節 準則特徵分析結果 ................. 58第三節 主測試 ........................... 63第四節 附加測試 ......................... 67第五節 測試結果比較 ..................... 82第五章 結論與討論.................................. 84參考文獻 .......................................... 86附錄一、IFRS 9準則原文及特徵 ....................... 94附錄二、問題原文 .................................. 112附錄三、加測試結果表 .............................. 130 zh_TW dc.format.extent 6613082 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0105353504 en_US dc.subject (關鍵詞) 語意網 zh_TW dc.subject (關鍵詞) 自然語言處理 zh_TW dc.subject (關鍵詞) 問答系統 zh_TW dc.subject (關鍵詞) 金融資產分類 zh_TW dc.subject (關鍵詞) 國際財務報導準則第9號 zh_TW dc.subject (關鍵詞) Semantic Web en_US dc.subject (關鍵詞) Natural Language Processing en_US dc.subject (關鍵詞) Question Answering en_US dc.subject (關鍵詞) Classification of Financial Assets en_US dc.subject (關鍵詞) IFRS 9 en_US dc.title (題名) 以自然語言處理建構基於語意網的金融資產分類問答系統：以IFRS 9為基礎準則 zh_TW dc.title (題名) Using Natural Language Processing and Semantic Web to Construct a Classification System for Financial Assets: an Example of IFRS 9 en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Aas, K., Jullum, M., & Løland, A. 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