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研究生:邱汾柔
研究生(外文):Fen-rou Ciou
論文名稱:運用字詞及概念頻率方法於不同結構文章上建構概念圖
論文名稱(外文):Term with Concept Frequency for Constructing Concept Maps from Different Types of Articles
指導教授:許中川許中川引用關係
指導教授(外文):Chung-Chian Hsu
學位類別:碩士
校院名稱:國立雲林科技大學
系所名稱:資訊管理系碩士班
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:41
中文關鍵詞:文本分析概念圖挖掘文字探勘基於概念的挖掘模型資訊萃取
外文關鍵詞:Concept map miningText analysisText miningConcept-based mining modelInformation extraction
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本文主要的目的是希望能從一篇文章中自動建構概念圖。一個好的概念圖能夠讓學生有效的了解文章的內容並且掌握重要的字詞。在自動建構的過程中,我們會將文章進行前處理包含去除標點符號、還原形態等,且會自動標記文章字詞的詞性,在處理上只抓取名詞或是名詞片語,再進行自動標記動詞參數的結構,對於較有貢獻的字詞進行抓取,找出較有意義的字詞,在計算過程上我們會針對計算該字詞在段落上及在動詞參數結構上出現的頻率,運用上述的概念,我們能夠有效的運用在TF-IPF、TCF-IPF及TCF-RPF三種方法上,進而計算各重要字詞的權重。在繪製時,我們將概念圖分為三層,在中間的第一層是整篇文章中的主要概念,第二層為各段落中最重要的概念,第三層則是為各段落中次要重要的概念。實驗部份主要是將三種方法自動建構概念圖系統與應用外語系學生進行評估,藉由計算系統與學生所抓取關鍵字的個數以及系統與學生交集的個數,來評估實驗的準確率。在實驗結果上,我們提出來的TCF-IPF及TCF-RPF優於基於TF-IDF的TF-IPF方法。
As globalization emerges, English becomes an important language. English learning has existed in various methods to learn. Traditionally, when learners read a long English article, it is time consuming to extract important. Therefore, we propose an approach constructing concept map to assist learners for improving reading in English. We developed a framework for concept mapping process. The framework consists of three steps. The first step is preprocessing which extracts noun words from an original article. The second step is automatically label in each sentence which may have one or more labeled verb argument structure. The third step, we count the number of the words in verb argument structures and each paragraph. Finally, we calculate the weight of the words and choose the highest weight of the words as main ideas. Using the three steps, we develop three methods which are TF-IPF, TCF-IPF and TCF-RPF. In experiments, we extract the important words among the three methods. We compare the result of manual extraction with the extraction result of our approach to evaluate the accuracy of concept map. In the experimental result, our method TCF-IPF and TCF-RPF are better than the TF-IPF base on the TF-IDF method.
中文摘要 i
ABSTRACT ii
誌 謝 iii
1. Introduction 1
2. Literature Review 3
2.1 Concept map 3
2.2 Automatic construction of concept maps 3
2.3 Statistical Approaches for Keyword Extraction 4
3. Method 6
3.1 Mining concept maps from texts 6
3.2 key term pre-process extraction 7
3.3 Automatic Semantic role labeling 8
3.4 Calculating conceptual term frequency in paragraph 10
3.5 Calculating key concept 11
3.5.1 The term frequency-inverse paragraph frequency 13
3.5.2 The term with concept frequency-inverse paragraph frequency 14
3.5.3 The term with concept frequency and remaining paragraph frequency 15
4. Experiment 18
4.1 Experimental Data 18
4.2 The experimental result of concept extraction 18
4.3 Different types of articles 24
4.4 Different analyses 24
5. Conclusion 26
References 27
Appendix A. Experimental article 29
[1]Chang, S.-N. (2007). Externalizing students'' mental models through concept maps. Journal of Biological Education, 41(3), 107-112.
[2]Cognition, Alberto Canas and J. D. Novak and F. M. Gonzalez and Alberto J. Canas and Marco Carvalho and Marco Arguedas and Machine. (2004). Mining The Web To Suggest Concepts During Concept map Construction. Universidad Publica de Navarra, 135--142.
[3]Horton, Phillip B., McConney, Andrew A., Gallo, Michael, Woods, Amanda L., Senn, Gary J., &; Hamelin, Denis. (1993). An investigation of the effectiveness of concept mapping as an instructional tool. Science Education, 77(1), 95-111. doi: 10.1002/sce.3730770107
[4]Kinchin, I. M. (2000). Concept-Mapping Activities To Help Students Understand Photosynthesis--and Teachers Understand Students. School science review, 82(n299).
[5]Novak, J. D., &; Gowin, D. B. (1984). Learning how to learn. Cambridge: Cambridge University Press.
[6]Novak, J.D., Mintzes, J., and Wandersee, J. (2000). Learning, Teaching, and Assessment: A Human Constructivist Perspective. in Assessing Science Understanding: A Human Constructivist View.
[7]Novak, Joseph D. (2010). Learning, Creating, and Using Knowledge: Concept Mapas as Facilitative Tools in Schools and Corporations. Journal of e-learning and knowledge society, 6(3), 21-30.
[8]Novak, JosephD. (1990). Concept maps and Vee diagrams: two metacognitive tools to facilitate meaningful learning. Instructional Science, 19(1), 29-52. doi: 10.1007/BF00377984
[9]Palmer, Paul Kingsbury and Martha. (2003). Propbank: The Next Level of Treebank. Proc. Workshop Treebanks and Lexical Theories.
[10]Porter, M. F. (1997). An algorithm for suffix stripping. In J. Karen Sparck &; W. Peter (Eds.), Readings in information retrieval (pp. 313-316): Morgan Kaufmann Publishers Inc.
[11]Reader, Will, &; Hammond, Nick. (1994). Computer-based tools to support learning from hypertext: concept mapping tools and beyond. Comput. Educ., 22(1-2), 99-106. doi: 10.1016/0360-1315(94)90078-7
[12]Salton, Gerard, &; Buckley, Christopher. (1988). Term-weighting approaches in automatic text retrieval. Inf. Process. Manage., 24(5), 513-523. doi: 10.1016/0306-4573(88)90021-0
[13]Shehata, S., Karray, F., &; Kamel, M. (2006, 18-22 Dec. 2006). Enhancing Text Clustering Using Concept-based Mining Model. Paper presented at the Data Mining, 2006. ICDM ''06. Sixth International Conference on.
[14]Shehata, S., Karray, F., &; Kamel, M. S. (2010). An Efficient Concept-Based Mining Model for Enhancing Text Clustering. Knowledge and Data Engineering, IEEE Transactions on, 22(10), 1360-1371. doi: 10.1109/TKDE.2009.174
[15]Stewart, J., Vankirk, J., &; Rowell, R. (1979). Concept maps: A tool for use in biology teaching. American Biology Teacher, 41(3), 171–175.
[16]Stoddart, Trish, Abrams, Robert, Gasper, Erika, &; Canaday, Dana. (2000). Concept maps as assessment in science inquiry learning - a report of methodology. International Journal of Science Education, 22(12), 1221-1246. doi: 10.1080/095006900750036235
[17]Tsai, C. C., Lin, S. S. J., &; Yuan, S. M. (2001). Students'' use of web-based concept map testing and strategies for learning. Journal of Computer Assisted Learning, 17(1), 72-84. doi: 10.1111/j.1365-2729.2001.00160.x
[18]Tseng, Shian-Shyong, Sue, Pei-Chi, Su, Jun-Ming, Weng, Jui-Feng, &; Tsai, Wen-Nung. (2007). A new approach for constructing the concept map. Computers Education, 49(3), 691-707. doi: 10.1016/j.compedu.2005.11.020
[19]Tseng, Yuen-Hsien, Chang, Chun-Yen, Rundgren, Shu-Nu Chang, &; Rundgren, Carl-Johan. (2010). Mining concept maps from news stories for measuring civic scientific literacy in media. Computers &; Education, 55(1), 165-177. doi: 10.1016/j.compedu.2010.01.002
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1. 25. 彭建文、張金鶚(2000),預期景氣與宣告效果對房地產景氣之影響,管理學報 17(2):343-368。
2. 25. 彭建文、張金鶚(2000),預期景氣與宣告效果對房地產景氣之影響,管理學報 17(2):343-368。
3. 23. 曾建穎、張金鶚及花敬群(2005),不同空間、時間住宅租金與其房價關聯性之研究—台北地區之實證現象分析,住宅學報第十四卷第二期,第27頁—49頁。
4. 23. 曾建穎、張金鶚及花敬群(2005),不同空間、時間住宅租金與其房價關聯性之研究—台北地區之實證現象分析,住宅學報第十四卷第二期,第27頁—49頁。
5. 12. 林祖嘉、林素菁(1994),台灣地區住宅需求價格彈性與所得彈性之估計,住宅學報,第2期,頁25-48。
6. 12. 林祖嘉、林素菁(1994),台灣地區住宅需求價格彈性與所得彈性之估計,住宅學報,第2期,頁25-48。
7. 10. 林祖嘉(1992),臺灣地區房租與房價關係之研究,臺灣銀行季刊,43(1),頁279-312。
8. 10. 林祖嘉(1992),臺灣地區房租與房價關係之研究,臺灣銀行季刊,43(1),頁279-312。
9. 6. 林英彥(2006),不動產估價(11版),文笙書局,臺北。
10. 6. 林英彥(2006),不動產估價(11版),文笙書局,臺北。
11. 2. 吳森田(1994 ),所得、貨幣與房價—近二十年來台北地區的觀察,住宅學報, 2:49-66。
12. 2. 吳森田(1994 ),所得、貨幣與房價—近二十年來台北地區的觀察,住宅學報, 2:49-66。
 
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