English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113303/144284 (79%)
Visitors : 50808125      Online Users : 804
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version
    政大機構典藏 > 商學院 > 會計學系 > 學位論文 >  Item 140.119/140974


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/140974


    Title: 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
    Authors: 李長瑋
    Li, Chang-Wei
    Contributors: 張祐慈
    周濟群
    Chang, Yu-Tzu
    Chou, Chi-Chun
    李長瑋
    Li, Chang-Wei
    Keywords: 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
    Date: 2022
    Issue Date: 2022-08-01 17:05:14 (UTC+8)
    Abstract: 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.
    Reference: Aas, K., Jullum, M., & Løland, A. (2021). Explaining individual predictions when features are dependent: More accurate approximations to Shapley values. Artificial Intelligence, 298, 103502. https://doi.org/10.1016/j.artint.2021.103502
    Adadi, A., & Berrada, M. (2018). Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access, 6, 52138–52160. https://doi.org/10.1109/ACCESS.2018.2870052
    Alkhateeb, F., Baget, J.-F., & Euzenat, J. (2009). Extending SPARQL with regular expression patterns: For querying RDF. Web Semantics, 7(2), 57–73. Scopus. https://doi.org/10.1016/j.websem.2009.02.002
    Allen, J. (1995). Natural language understanding (2nd ed.). USA: Benjamin-Cummings Publishing Co., Inc.
    Angelov, P., & Soares, E. (2020). Towards explainable deep neural networks (xDNN). Neural Networks, 130, 185–194. https://doi.org/10.1016/j.neunet.2020.07.010
    Antoniou, G., & van Harmelen, F. (2004). Web Ontology Language: OWL. In International Handbooks on Information Systems. Handbook on Ontologies (pp. 67–92). Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-24750-0_4
    Bao, Y., Ke, B., Li, B., Yu, Y. J., & Zhang, J. (2020). Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach. Journal of Accounting Research, 58(1), 199–235. https://doi.org/10.1111/1475-679X.12292
    Barniv, R., Agarwal, A., & Leach, R. (1997). Predicting the outcome following bankruptcy filing: A three-state classification using neural networks. Intelligent Systems in Accounting, Finance and Management, 6(3), 177–194. https://doi.org/10.1002/(SICI)1099-1174(199709)6:3<177::AID-ISAF134>3.0.CO;2-D
    Barwise, J. (1977). An Introduction to First-Order Logic. In HANDBOOK OF MATHEMATICAL LOGIC: Vol. 90. Studies in Logic and the Foundations of Mathematics (pp. 5–46). Elsevier. https://doi.org/10.1016/S0049-237X(08)71097-8
    Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific American, 284(5), 34–43. Scopus. https://doi.org/10.1038/scientificamerican0501-34
    Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2020). Using machine learning to detect misstatements. Review of Accounting Studies. https://doi.org/10.1007/s11142-020-09563-8
    Brahim Batouche, Claire Gardent, & Anne Monceaux. (2015). Parsing Text into RDF graphs. Actas Del XXXI Congreso de La Sociedad Española Para El Procesamiento Del Lenguaje Natural. Presented at the Sociedad Española para el Procesamiento del Lenguaje Natural Conference 2015, Alicante. Retrieved from https://gplsi.dlsi.ua.es/sepln15/es/node/64
    Brown, P. F., Pietra, V. J. D., Pietra, S. A. D., & Mercer, R. L. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2), 263–311.
    Buil-Aranda, C., Arenas, M., Corcho, O., & Polleres, A. (2013). Federating queries in SPARQL 1.1: Syntax, semantics and evaluation. Journal of Web Semantics, 18, 1–17. Scopus. https://doi.org/10.1016/j.websem.2012.10.001
    Calijorne Soares, M. A., & Parreiras, F. S. (2020). A literature review on question answering techniques, paradigms and systems. Journal of King Saud University - Computer and Information Sciences, 32(6), 635–646. https://doi.org/10.1016/j.jksuci.2018.08.005
    Cambria, E., & White, B. (2014). Jumping NLP Curves: A Review of Natural Language Processing Research. IEEE Computational Intelligence Magazine, 9(2), 48–57. https://doi.org/10.1109/MCI.2014.2307227
    Chanaa, A., & El Faddouli, N.-E. (2020). BERT and Prerequisite Based Ontology for Predicting Learner’s Confusion in MOOCs Discussion Forums. Artificial Intelligence in Education, 54–58. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-52240-7_10
    Chen, H., Lundberg, S., & Lee, S.-I. (2021). Explaining Models by Propagating Shapley Values of Local Components. In Studies in Computational Intelligence. Explainable AI in Healthcare and Medicine: Building a Culture of Transparency and Accountability (pp. 261–270). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-53352-6_24
    Chen, Y. (2022). Abnormal Data Monitoring and Analysis Based on Data Mining and Neural Network. Journal of Sensors, 2022, e2635819. https://doi.org/10.1155/2022/2635819
    Chowdhary, K. R. (2020). Natural Language Processing. In Fundamentals of Artificial Intelligence (pp. 603–649). New Delhi: Springer India. https://doi.org/10.1007/978-81-322-3972-7_19
    Cooper, L. A., Holderness, D. K., Jr., Sorensen, T. L., & Wood, D. A. (2019). Robotic Process Automation in Public Accounting. Accounting Horizons, 33(4), 15–35. https://doi.org/10.2308/acch-52466
    Covaci, F., Buchmann, R., & Dragos, R. (2022, February 3). Towards a Knowledge Graph-specific Definition of Digital Transformation: An Account Networking View for Auditing. 2, 637–644. SCITEPRESS. https://doi.org/10.5220/0010875000003116
    Dieber, J., & Kirrane, S. (2020, November 30). Why model why? Assessing the strengths and limitations of LIME. arXiv. https://doi.org/10.48550/arXiv.2012.00093
    Dyer, M. G. (1995). Connectionist Natural Language Processing: A Status Report. In The Springer International Series In Engineering and Computer Science. Computational Architectures Integrating Neural And Symbolic Processes: A Perspective on the State of the Art (pp. 389–429). Boston, MA: Springer US. https://doi.org/10.1007/978-0-585-29599-2_12
    Figueroa, A., & Neumann, G. (2016). Context-aware semantic classification of search queries for browsing community question–answering archives. Knowledge-Based Systems, 96, 1–13. https://doi.org/10.1016/j.knosys.2016.01.008
    Haenlein, M., & Kaplan, A. (2019). A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
    Heath, T., & Bizer, C. (2011). Linked Data: Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web: Theory and Technology, 1(1), 1–136. https://doi.org/10.2200/S00334ED1V01Y201102WBE001
    Hutchins, J. (2006). The history of machine translation in a nutshell. Retrieved from https://www.semanticscholar.org/paper/The-history-of-machine-translation-in-a-nutshell-Hutchins/0965ae7b0fb9c709b37a2c6231cb9db6a657672b
    Joachims, T. (2002). Learning to Classify Text Using Support Vector Machines. Springer Science & Business Media.
    Kitaev, N., & Klein, D. (2018). Constituency Parsing with a Self-Attentive Encoder. ArXiv:1805.01052 [Cs]. Retrieved from http://arxiv.org/abs/1805.01052
    Kurzweil, R. (2006). The singularity is near: When humans transcend biology.
    Lavinia-Mihaela, C. (2019). How Ai Can Be Part of Solving Accounting and Business Issues? International Multidisciplinary Scientific GeoConference : SGEM, 19(2.1), 305–312. Sofia, Bulgaria: Surveying Geology & Mining Ecology Management (SGEM). https://doi.org/10.5593/sgem2019/2.1/S07.040
    Leitner-Hanetseder, S., Lehner, O. M., Eisl, C., & Forstenlechner, C. (2021). A profession in transition: Actors, tasks and roles in AI-based accounting. Journal of Applied Accounting Research, 22(3), 539–556. https://doi.org/10.1108/JAAR-10-2020-0201
    Li, F. (2010). The Information Content of Forward-Looking Statements in Corporate Filings—A Naïve Bayesian Machine Learning Approach. Journal of Accounting Research, 48(5), 1049–1102. https://doi.org/10.1111/j.1475-679X.2010.00382.x
    Lopez V., Uren V., Sabou M., & Motta E. (2011). Is Question Answering fit for the Semantic Web?: A survey. Semantic Web, 2(2), 125–155. https://doi.org/10.3233/SW-2011-0041
    McCorduck, P. (2004). Machines who think: A personal inquiry into the history and prospects of artificial intelligence. Natick, Mass.: A.K. Peters.
    Miller, G. A. (1995). WordNet: A lexical database for English. Communications of the ACM, 38(11), 39–41. https://doi.org/10.1145/219717.219748
    Nigam, K., Mccallum, A. K., Thrun, S., & Mitchell, T. (2000). Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2), 103–134. https://doi.org/10.1023/A:1007692713085
    Olshannikova, E., Olsson, T., Huhtamäki, J., & Kärkkäinen, H. (2017). Conceptualizing Big Social Data. Journal of Big Data, 4(1), 3. https://doi.org/10.1186/s40537-017-0063-x
    O’Riain, S., McCrae, J., Cimiano, P., & Spohr, D. (2015). Using SPIN to Formalise XBRL Accounting Regulations on the Semantic Web. The Semantic Web: ESWC 2012 Satellite Events, 58–72. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-662-46641-4_5
    Pannu, A., & Student, M. (2015). Artificial Intelligence and its Application in Different Areas. International Journal of Engineering and Innovative Technology, 4(10). Retrieved from https://www.semanticscholar.org/paper/Artificial-Intelligence-and-its-Application-in-Pannu-Student/9a4d9a755134e612854db1897c03adb3983413df
    Perols, J. (2011). Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms. AUDITING: A Journal of Practice & Theory, 30(2), 19–50. https://doi.org/10.2308/ajpt-50009
    Petkov, R. (2019). Artificial Intelligence (AI) and the Accounting Function—A Revisit and a New Perspective for Developing Framework. Journal of Emerging Technologies in Accounting, 17(1), 99–105. https://doi.org/10.2308/jeta-52648
    Porteous, I., Newman, D., Ihler, A., Asuncion, A., Smyth, P., & Welling, M. (2008). Fast collapsed gibbs sampling for latent dirichlet allocation. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 569–577. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/1401890.1401960
    Qiu, J. (2021). Analysis of Human Interactive Accounting Management Information Systems Based on Artificial Intelligence. Journal of Global Information Management (JGIM), 30(7), 1–13. https://doi.org/10.4018/JGIM.294905
    Quinn, T. P., Jacobs, S., Senadeera, M., Le, V., & Coghlan, S. (2022). The three ghosts of medical AI: Can the black-box present deliver? Artificial Intelligence in Medicine, 124, 102158. https://doi.org/10.1016/j.artmed.2021.102158
    Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th edition). Hoboken: Pearson.
    Samek, W., & Müller, K.-R. (2019). Towards Explainable Artificial Intelligence. In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (pp. 5–22). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-28954-6_1
    Schank, R. C. (1975). Conceptual information processing.
    Setzu, M., Guidotti, R., Monreale, A., Turini, F., Pedreschi, D., & Giannotti, F. (2021). GLocalX - From Local to Global Explanations of Black Box AI Models. Artificial Intelligence, 294, 103457. https://doi.org/10.1016/j.artint.2021.103457
    Shi, N., Zeng, Q., Lee, R., Shi, N., Zeng, Q., & Lee, R. (2020). XAI Language Tutor—A XAI-based Language Learning Chatbot using Ontology and Transfer Learning Techniques. International Journal on Natural Language Computing (IJNLC), 9(5), 1. https://doi.org/10.5121/ijnlc.2020.9501
    Song, F., Zacharewicz, G., & Chen, D. (2013). An ontology-driven framework towards building enterprise semantic information layer. Advanced Engineering Informatics, 27(1), 38–50. https://doi.org/10.1016/j.aei.2012.11.003
    Turing, A. M. (1937). On Computable Numbers, with an Application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, s2-42(1), 230–265. https://doi.org/10.1112/plms/s2-42.1.230
    Turing, A. M. (1950). I.—COMPUTING MACHINERY AND INTELLIGENCE. Mind, LIX(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433
    Veena G, Deepa Gupta, Akshay Anil, & Akhil S. (2019). An Ontology Driven Question Answering System for Legal Documents. 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), 1, 947–951. https://doi.org/10.1109/ICICICT46008.2019.8993168
    Weygandt, J. J., Kimmel, P. D., & Kieso, D. E. (2022). Financial Accounting with International Financial Reporting Standards. Wiley.
    Yu R., & Alì G. S. (2019). What’s Inside the Black Box? AI Challenges for Lawyers and Researchers. Legal Information Management, 19(1), 2–13. https://doi.org/10.1017/S1472669619000021
    吳泰廷、楊文新、崔文（2012）。語意網、鏈結資料與開放資料之實務技術與應用。電腦與通訊，(145)，102–109。
    周濟群、周國華、戚玉樑（2017）。以知識本體技術塑模會計知識之研究。電子商務學報，19(1)，51–81。https://doi.org/10.6188/JEB.2017.19(1).03
    戚玉樑（2005）。以本體技術為基礎的知識庫建置程序及其應用。資訊、科技與社會學報，5(2)，1–18。
    江美艷、陳欣怡（2016）。金融工具大變革──談IFRS 9。證券暨期貨月刊，34(4)，30–43。
    Description: 博士
    國立政治大學
    會計學系
    105353504
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0105353504
    Data Type: thesis
    DOI: 10.6814/NCCU202200757
    Appears in Collections:[會計學系] 學位論文

    Files in This Item:

    File Description SizeFormat
    350401.pdf6458KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback