博碩士論文 102423026 詳細資訊




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姓名 張明竣(Ming-Chun Chang)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 應用相關回饋之語詞資訊於概念建立之方法
(The application of the term information residing in relevance feedback for concept construction)
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摘要(中) 過去的概念萃取研究中,對於一篇文件應該萃取多少概念去表達,沒有一個依據。因此,本研究旨在探討概念萃取數量與文件集離散程度間的關聯,並且利用公開資料集TREC-6做實驗,驗證其是否對文件分類效能上有所提升。如果將文件集分群後,文件平均分佈在各個群中,代表文件集的離散程度很高,本研究假設應該萃取更多概念才能夠足以表達文件集中大部份文章。反之如果文件集中在某幾個群中,代表文件集的離散程度很低,表示文件的分佈是很集中的,萃取少量概念就足以表達大部份文章。在本研究中提出動態概念萃取策略,利用文件分群得知其離散程度,並利用此數據動態控制萃取的概念數量,經實驗驗證可以初步證實本研究所提出的動態概念萃取策略,對於文件分類上的效能有進一步提升。
摘要(英) In the past, we did not have a method to determine how many concpts to represent the document. The aim of this study is to discuss the relation between the number of concepts extraction and the dispersion of document dataset. This study uses public document dataset TREC-6 to validate the effectiveness of text classification. This study proposes that a document dataset has high dispersion if the documents distribute evenly in a cluster. In this case, this study assumes that more concepts are needed to represent the document. On the contrary, if the documents has a centralized distribution in a cluster, the document dataset has low dispersion. In this case, this study assumes that less concepts are needed to represent the document. This study proposes a dynamic concept extraction method which applies the degree of dispersion as the basis to dynamically determine the number of concepts. Empirical results show that the proposes method can improve the effectiveness in text classification.
關鍵字(中) ★ 概念萃取
★ 文件概念化
★ 相關回饋
★ 向量空間模型
★ 文件集離散程度
關鍵字(英) ★ concept extraction
★ bag-of-concepts
★ relevance feedback
★ vector space model
★ dispersion of document dataset
論文目次 一、緒論 1
1.1 研究背景與動機 1
1.2 研究目的 1
1.3 研究範圍 2
1.4 研究限制 2
1.5 論文架構 2
二、文獻探討 3
2.1 向量空間模型 (Vector Space Model) 3
2.1.1 Bag-of-Words 3
2.1.1 Bag-of-Concepts 4
2.1.1 Combination Bag of Words & Bag of Concepts 5
2.2 相關回饋 (Relevance Feedback) 5
2.3 概念萃取 (Concept Extraction) 相關研究 6
2.3.1 需要外部資源協助概念建立 6
2.3.2 不需要外部資源協助概念建立 7
2.4 分群相關研究 8
2.4.1 K-means 8
2.4.2 階層式分群法 (Hierarchical Clustering) 8
2.4.3 分群假說 (Cluster Hypothesis) 9
三、研究方法 10
3.1 系統架構 11
3.2 文件集過濾流程 12
3.3 文件概念化流程 14
3.3.1 概念萃取 15
3.3.2 文件概念化 17
3.4 文件分類器 19
四、實驗結果評估與分析 20
4.1、實驗環境 20
4.2、實驗資料 20
4.3、實驗評估 23
4.4、實驗設計與流程 24
4.4.1、模型一:只利用正向概念表示文件 24
4.4.2 模型一實驗結果討論 25
4.4.3、模型二:只利用負向概念表示文件 28
4.4.4 模型二實驗結果討論 29
4.4.5、模型三:利用完整正向、負向概念表示文章 32
4.4.6 模型三實驗結果討論 33
五、結論 36
5.1、結論與研究貢獻 36
5.2、未來研究方向 37
參考文獻 38
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[19] D. Harman, ′Relevance feedback revisited′, in Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval, Copenhagen, Denmark, June 21-24, 1992, pp. 1-10.
[20] I. RUTHVEN and M. LALMAS, ′A survey on the use of relevance feedback for information access systems′, Knowl. Eng. Rev., vol. 18, no. 2, pp. 95-145, 2003.
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指導教授 周世傑(Shih-Chieh Chou) 審核日期 2015-7-27
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