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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊裕澤(Yu-Zeh Joung) | |
| dc.contributor.author | Jia-Yu Chen | en |
| dc.contributor.author | 陳佳瑜 | zh_TW |
| dc.date.accessioned | 2021-06-17T06:07:12Z | - |
| dc.date.available | 2023-01-15 | |
| dc.date.copyright | 2019-01-15 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-01-08 | |
| dc.identifier.citation | REFERENCE
[1] Kontostathis A., Galitsky L.M., Pottenger W.M., Roy S., Phelps D.J. (2004) A Survey of Emerging Trend Detection in Textual Data Mining. In: Berry M.W. (eds) Survey of Text Mining. Springer, New York, NY. [2] Allan, J. (2002). Introduction to Topic Detection and Tracking. In J.Allan (Ed.), Topic Detection and Tracking: Event-based Information Organization (pp. 1–16). Boston, MA: Springer US. [3] 殷蜀梅（2008）。判斷新興研究趨勢的技術方法分析[Analysis of the Methods for Detecting Emerging Trend]。情報科學，26（4），536-540。 [4] Fujita, K., Kajikawa, Y., Mori, J., & Sakata, I. (2014). Detecting research fronts using different types of weighted citation networks. Journal of Engineering and Technology Management, 32, 129–146. [5] Morinaga, S., & Yamanishi, K. (2004). Tracking Dynamics of Topic Trends Using a Finite Mixture Model. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 811–816). New York, NY, USA: ACM. [6] Le, M.-H., Ho, T.-B., & Nakamori, Y. (2005). Detecting emerging trends from scientific corpora. International Journal of Knowledge and Systems Sciences, 2(2), 53–59. [7] He, Qi & Chen, Bi & Pei, Jian & Qiu, Baojun & Mitra, Prasenjit & Lee Giles, C. (2009). Detecting topic evolution in scientific literature: How can citations help?. International Conference on Information and Knowledge Management, Proceedings. 957-966. [8] Blei, D. M., Ng, A. Y., &Jordan, M. I. (2003). Latent Dirichlet Allocation. J. Mach. Learn. Res., 3, 993–1022. [9] Small, H., Boyack, K. W., & Klavans, R. (2014). Identifying emerging topics in science and technology. In Research Policy (Vol. 43, pp. 1450–1467). [10] Bolelli, L., Ertekin, Ş., & Giles, C. L. (2009). Topic and trend detection in text collections using latent dirichlet allocation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5478 LNCS, pp. 776–780). [11] Wang, X., & McCallum, A. (2006). Topics over Time: A Non-Markov Continuous-Time Model of Topical Trends. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD 06 (Vol. 72, p. 424). New York, New York, USA: ACM Press. [12] Pottenger, W. M., Kim, Y.-B., & Meling, D. D. (2001). HDDITM: Hierarchical Distributed Dynamic Indexing. In R. L.Grossman, C.Kamath, P.Kegelmeyer, V.Kumar, &R. R.Namburu (Eds.), Data Mining for Scientific and Engineering Applications (pp. 319–333). Boston, MA: Springer US. [13] Pottenger, W. M., &Yang, T.-H. (2001). Detecting emerging concepts in textual data mining. In M. W.Berry (Ed.), Computational Information Retrieval (pp. 89–105). Philadelphia, PA, USA: Society for Industrial and Applied Mathematics. [14] Kanagasabi, R., & Ah-Hwee, T. (2011). Topic Detection, Tracking and Trend Analysis Using Self-organizing Neural Networks. Advances in Knowledge Discovery and Data Mining, 102–107. [15] 陳仕吉（2009）。科學研究前沿探測方法綜述[Survey of Approaches to Research Front Detection]。現代圖書情報技術，9，28-33。 [16] Shibata, N., Kajikawa, Y., Takeda, Y., &Matsushima, K. (2008). Detecting emerging research fronts based on topological measures in citation networks of scientific publications. Technovation, 28, 758–775. [17] Shibata, Naoki & Kajikawa, Yuya & Takeda, Yoshiyuki & Matsushima, Katsumori. (2009). Comparative Study on Methods of Detecting Research Fronts Using Different Types of Citation. JASIST. 60. 571-580. [18] Fujimagari, H., & Fujita, K. (2015). Regular Paper Detecting Research Fronts Using Neural Network Model for Weighted Citation Network Analysis. Journal of Information Processing, 23(6), 753–758. [19] Morrison, A., & Rao, A. (2017, September 12). Machine learning evolution (infographic). Retrieved November 26, 2018, from http://usblogs.pwc.com/emerging-technology/machine-learning-evolution-infographic/ | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71706 | - |
| dc.description.abstract | 過去，人們自閱讀相關的期刊論文、專利或報導等資料中歸納得出研究領域的主題變化。然而隨著資訊爆炸的時代來臨，大量成長的文獻資料使得人工逐篇閱讀勞心勞力，主題演變的觀測變得更加地困難。因此，開發一套可協助使用者觀察研究主題演變的方法相當重要。
本研究提出一個新的研究主題引用網路，取代以往將單篇文獻作為節點並以文獻之間的引用關係作為連結依據的研究網路。我們抽取出文獻中作者所定義之關鍵字作為新引用網路的節點，並重新定義節點之間的引用關係以及權重。權重低於指定閥值的連結將會被刪除，而後針對網路內的節點進行聚類分析。每群節點集合是一個研究主題，藉由集合內的關鍵字內容可以得知研究主題的技術細節。最後，我們藉由檢測不同年度間的研究主題之引用關係來觀察其是否有演化關係。實驗結果顯示，新提出的引用網路可以從大量的文獻資料中抽取出可利用的研究主題，並協助領域內的專家觀察研究主題的演變。 | zh_TW |
| dc.description.abstract | In the past, people consumed related research thesis, journal papers or patent specifications to conclude the research trend of a topic area. However, with the coming of information explosion, consuming such a large number of data only by human seems to be inefficient. Therefore, it is important to develop a method to help users observe the evolution of research topics. In this study, we propose a novel citation network for detecting research trend to replace the past citation network that use a single document as a node and the citation relationships between documents as links. We extract the keywords which were specified by authors from the documents and redefine the citation relationships between nodes as well as the weight of edges. After deleting edges whose weight is lower than designated threshold, nodes in the final citation network would be clustered to find the research topics. Each cluster of nodes is considered as a research topic. The details of a research topic can be realized from the keywords in the cluster. Finally, we are able to observe the evolution of a topic area by checking the relationships among research topics in different years. The experiment shows that the proposed novel citation network can extract useful research topics from numerous literature and assist domain experts to observe the change of research topics. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T06:07:12Z (GMT). No. of bitstreams: 1 ntu-108-R05725011-1.pdf: 1412103 bytes, checksum: 34dfc4130195607741fe710c05c7483b (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 Chapter 2 Related Works 7 2.1 Topic representation 8 2.2 Topic identification and verification 11 2.2.1 Methods based on probabilistic models 12 2.2.2 Methods based on artificial neural networks 14 2.2.3 Methods based on citation networks 16 Chapter 3 Methodology 19 3.1 Citation network 19 3.2 Citation relationship 20 3.3 Citation weight 21 3.4 Normalization of citation weight 24 3.5 Research topic detection 27 Chapter 4 Experiment 30 4.1 Data collection 30 4.2 Results 31 Chapter 5 Conclusion 42 REFERENCE 44 | |
| dc.language.iso | en | |
| dc.subject | 引用關係 | zh_TW |
| dc.subject | 引用網路 | zh_TW |
| dc.subject | 研究主題演變 | zh_TW |
| dc.subject | 研究趨勢 | zh_TW |
| dc.subject | research trend | en |
| dc.subject | citation relationship | en |
| dc.subject | citation network | en |
| dc.subject | research topic evolution | en |
| dc.title | 一個新的研究主題演變引用網路 | zh_TW |
| dc.title | A Novel Citation Network for Research Topic Evolution | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 107-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 盧信銘(Hsin-Min Lu),曹承礎(Seng-Cho Chou) | |
| dc.subject.keyword | 引用關係,引用網路,研究主題演變,研究趨勢, | zh_TW |
| dc.subject.keyword | citation relationship,citation network,research topic evolution,research trend, | en |
| dc.relation.page | 47 | |
| dc.identifier.doi | 10.6342/NTU201900022 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-01-08 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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|---|---|---|---|
| ntu-108-1.pdf 未授權公開取用 | 1.38 MB | Adobe PDF |
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