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    政大機構典藏 > 商學院 > 會計學系 > 學位論文 >  Item 140.119/140974


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    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.
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    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

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