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系統識別號 U0026-1007201900541200
論文名稱(中文) 使用語意分析提升Help Desk處理問題效能
論文名稱(英文) Use Semantic Analysis to Improve the Performance of Help Desk Problems
校院名稱 成功大學
系所名稱(中) 工業與資訊管理學系碩士在職專班
系所名稱(英) Department of Industrial and Information Management (on the job class)
學年度 107
學期 2
出版年 108
研究生(中文) 陳正煌
研究生(英文) Jeng-Huang Chen
學號 R37061210
學位類別 碩士
語文別 中文
論文頁數 76頁
口試委員 指導教授-王惠嘉
口試委員-李昇暾
口試委員-劉任修
口試委員-郭俊良
中文關鍵字 問答系統  主題模型  語意分析  文件分群  文字摘要 
英文關鍵字 Question and answer system  Topic model  Semantic analysis  Document Clustering  Text summarization 
學科別分類
中文摘要 隨著資訊系統在企業越來越普及與重要,專門處理資訊相關問題的Help Desk人員也對企業的工作效率有重大的影響,然而企業對於Help Desk的重視依然保守,為了減低Help Desk高流動率的負面影響以及提升企業工作效率,對於企業現有的User Help Desk問題利用文字分析方法來處理系統的歷史處理問題紀錄集來挖掘有價值、可重複使用的資訊是企業值得投入的。
因在傳統的關鍵字查詢結果不是不夠精準就是相同語意但不同用詞的語意問題,使得查尋條件太難設定,為了分析語意關連問題,本研究採用E-HowNet語意知識庫來轉換中文詞彙之語意關係,再使用主題模型LDA(Latent Dirichlet Allocation)方法來找出每篇文章所代表的主題,依題來將相似的問題聚集起來,取出這些問題的回答紀錄進行分群並萃取摘要,並依主題關連性依序呈現給使用者,經實作驗證後,轉換語意時加入完整詞性之篩選比無語意處理提升Precision約8.5%,而用LDA訓練好的主題模型取出相同主題之問題來計算,雖然Precision從99%降為92%,但花費時間可縮短為原本的1/34,而本研究文集屬於短文集,因此句子關聯度門檻值不宜設太高避免摘要萃取失敗,建議值為0.05,此外還發現AP Cluster分群之摘要效果比K-means好。
英文摘要 With the increasing popularity and importance of information systems in enterprises, in order to reduce the negative impact of the high turnover rate of Help Desk personnel who specialize in information-related issues and improve the efficiency of enterprises, text analysis methods are used to record the history of problem-solving systems. It is worth investing in the collection of valuable, reusable information. In order to analyze the semantic relationship, this study uses the E-HowNet semantic knowledge base to convert the semantic relationship of Chinese vocabulary, and then uses the topic model LDA method to find out the topic represented by each article, and gather similar questions according to the topic. The answer records of these questions are taken out and the abstracts are extracted and presented to the users according to the topic relevance. After the verification, the precision of the conversion of semantic meanings into the complete part of speech screening is improved by 8.5% than the no semantic processing. The LDA-trained topic model takes the same subject problem to calculate. Although precision is reduced from 99% to 92%, the time spent can be shortened to the original 1/34, and the study essay belongs to the short essay, so the threshold of sentence relevance should not be set too high to avoid the abstract extraction failure. In addition, it is found that the summary effect of AP Cluster is better than K-means.
論文目次 摘要 I
Extended Abstract II
誌謝 VI
第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 5
1.3 研究範圍與限制 6
1.4 研究流程 6
1.5 論文架構 7
第2章 文獻探討 9
2.1 問答系統 9
2.2 向量空間模型 10
2.3 語意分析 11
2.3.1 奇異值分解 11
2.3.2 非負矩陣拆解法 12
2.3.3 本體論 13
2.3.4 主題模型 14
2.3.5 綜合比較 18
2.4 文件分群 18
2.4.1 階層式分群演算法 19
2.4.2 分割式分群演算法 20
2.4.3 密度分群法 20
2.4.4 單通道法 21
2.4.5 AP聚類演算法 21
2.5 文字摘要 23
2.6 小結 26
第3章 研究方法 27
3.1 研究架構 27
3.2 資料前置處理模組 30
3.3 語意分析模組 31
3.3.1 語意關係轉換 32
3.3.2 主題模型分析 33
3.4 答案推薦模組 36
3.4.1 問題分群與排名 37
3.4.2 候選答案分群 38
3.4.3 候選答案摘要 39
第4章 實作及驗證 42
4.1 系統建置環境 42
4.1.1 資料收集 43
4.1.2 資料前處理 43
4.1.3 語意分析 43
4.1.4 答案推薦 44
4.2 實驗設計 44
4.2.1 資料來源 44
4.2.2 比較對象 44
4.2.3 評估指標 45
4.2.4 實驗結果 47
4.3 問題查詢之答案推薦範例 59
第5章 結論與未來研究方向 60
5.1 研究成果 60
5.2 未來研究方向 62
參考文獻 64
附錄一 專家自訂停用字集 68
附錄二 專門術語詞性字典 69
附錄三 停用詞詞性字典 73
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