研究生: |
徐一正 Hsu, Yi-Cheng |
---|---|
論文名稱: |
依讀者角色提升文件內容易讀性之技術文件內容轉化模式 A Role-based Approach for Readability Enhancement and Content Conversion of Technical Documents |
指導教授: |
侯建良
Hou, Jiang-Liang |
口試委員: |
楊士霆
吳士榤 |
學位類別: |
碩士 Master |
系所名稱: |
工學院 - 工業工程與工程管理學系 Department of Industrial Engineering and Engineering Management |
論文出版年: | 2023 |
畢業學年度: | 111 |
語文別: | 中文 |
論文頁數: | 351 |
中文關鍵詞: | 類神經網路 、文件內容之易讀性評估 、文件內容轉化 |
外文關鍵詞: | Document Readability Analysis, Text Readability Enhancement, Neural Network |
相關次數: | 點閱:128 下載:0 |
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於一企業之產品研發專案中,負責該專案之專案工程師往往需事先閱讀大量技術文件,並將研發人員所撰寫之技術文件內容轉化為更簡潔的表達方式,以利高階管理者有效瞭解專案內容。然而,技術文件通常包含許多技術性用語,由於研發人員與專案工程師之專業領域不同,專案工程師往往難以快速且準確地將研發人員所撰寫之技術文件內容轉化為較簡潔的表達方式。此外,專案工程師需耗費時間及精力閱讀大量技術文件,並判定此些文件內容的資訊豐富程度及易讀程度,才能將技術文件轉化為審查此專案之主管或其他閱讀者易於解讀的說明資料。為解決前述問題,本研究乃發展一套「依讀者角色提升文件內容易讀性之技術文件內容轉化」模式,以協助專案工程師有效地將技術文件轉化為高階管理者或其他閱讀者易於解讀的說明資料,進而協助高階管理者或其他非相關技術背景人士理解原技術文件之內容重點。
本研究所提出之「依讀者角色提升文件內容易讀性之技術文件內容轉化」模式可分為「技術內容及其所對應之廣告內容解析」前置階段及「目標技術文件內容轉化」方法論。於「技術內容及其所對應之廣告內容解析」前置階段,本研究乃先釐清各類產品相關之技術內容及廣告內容中的技術特徵,並解析各特徵內容於技術內容及廣告內容中之表達方式。以前述過程之結果為基礎,本研究歸納各特徵內容由技術內容轉化為廣告內容之轉化原則,以利後續方法論可藉由此些原則轉化目標技術內容。之後,本研究乃利用類神經網路方法建立技術資訊密度影響因子及技術資訊密度值之關聯模型,以利後續藉由此關聯模型分析目標技術內容之資訊豐富程度。而「目標技術文件內容轉化」方法論乃擷取目標技術內容所包含的技術特徵值,並利用前置階段所建立之關聯模型推估目標技術內容的技術資訊密度值。之後,此模式提供目標技術內容之資訊豐富程度的分析結果及技術特徵表達方式的轉化建議,並將此些資訊以視覺化方式呈現,以協助專案工程師有效地將技術文件轉化為高階管理者或其他閱讀者易於解讀的說明資料。
The project engineer who is responsible for a product development project often needs to read the technical documents written by research and development (R&D) engineers and convert the technical content into concise and easily interpretable content. However, due to the different professional domains between R&D engineers and project engineers, the latter often struggle to efficiently and accurately convert the technical content into concise and easily interpretable content. Additionally, the project engineer has to invest time and effort in reading numerous technical documents and enhancing the readability of these documents. In order to solve the above problems, this study develops a “Readability Enhancement and Content Conversion” model. The proposed model extracts the key information from technical documents and utilizes the neural network to assess the readability of technical documents. After that, the proposed model provides recommendations for converting the technical content in order to enhance the readability of technical content. Furthermore, the proposed model visualizes the key information and the content readability analysis results. Based on the proposed model, the project engineer can effectively convert the technical content into concise and easily interpretable content.
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