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研究生:楊宛蓁
研究生(外文):Wan-Chen Yang
論文名稱:基於矩陣分解的主題演進發現
論文名稱(外文):Topic Evolution Discovery based on Regularized Matrix Factorization
指導教授:康藝晃
指導教授(外文):Yihuang Kang
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊管理學系研究所
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:2018
畢業學年度:106
語文別:英文
論文頁數:43
中文關鍵詞:多層主題主題分類主題演進發現非負矩陣分解主題模型階層式非負矩陣分解階層式模型
外文關鍵詞:Topic modelHierarchical modelNon-negative matrix factorizationHierarchical Non-negative matrix factorizationTopic classificationTopic evolution discoveryMulti-layer topic
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在這項研究中,我們提出使用階層式非負矩陣分解來探討主題間的關係,在階層式的結構中,越低的層數主題會越具體,而越高的層數中的主題則越抽象。藉由從較具體的主題,一層一層至抽象主題的這個過程,可以漸進式的了解主題間的關係。此外我們的模型可以觀察主題的演變,主題是會隨著時間演進、合併以及消失的。
在我們的實驗中,我們使用非負矩陣分解,去分解各階層間,主題以及術語組成的矩陣,探討每年中各階層間的主題演變,我們藉由樹和網路將主題間的關係視覺化,藉由網路的方式,可以去呈現出主題以及術語間的關係,而主題樹可以表現出各個階層間的主題與主題之間的關係。
In this thesis, we propose Hierarchical Non-negative Matrix Factorization(hNMF) to discover the hierarchical correlation among topics. The topics in lower layers are more concrete, whereas ones in higher layers are more abstract. Furthermore, our model could discover the evolution of topics, recognizing whether topics arise, merge or disappear. In our experiment, we applied nonnegative matrix factorization(NMF) on document-term matrix in each layer, detecting the evolution of topics in several years. We visualized the relationships by graphical model such as networks and trees. Topic networks present the communication between topics and terms, and topic trees reveal the hierarchical relationship among topics.
論文審定書 i
中文摘要 ii
英文摘要 iii
1. INTRODUCTION 1
2. BACKGROUND AND RELATED WORK 3
2.1 NON-NEGATIVE MATRIX FACTORIZATION (NMF) 3
2.2 HIERARCHICAL NON-NEGATIVE MATRIX FACTORIZATION(HNMF) 5
3. METHODOLOGY 5
3.1 HOW MANY KS? 6
3.2 HIERARCHICAL NON-NEGATIVE MATRIX FACTORIZATION 7
3.3 FIND THE EVOLUTION OF TOPIC 9
3.4 HNMF RELATIONSHIP BY NETWORK 11
4. EXPERIMENT 12
4.1 FIND TOPICS BY NMF WITH SAME K 12
4.2.1 TOPIC MODELING WITH HIERARCHICAL NON-NEGATIVE MATRIX FACTORIZATION 17
4.2.2 TOPIC TREE OF HIERARCHICAL NON-NEGATIVE MATRIX FACTORIZATION 27
4.3 DISPLAY TOPIC – HNMF RELATIONSHIP BY NETWORK 29
5. DISCUSSION 32
6. CONCLUSION 34
7. REFERENCE 34
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Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27(1), 55–71.
Choo, J., Lee, C., Reddy, C. K., & Park, H. (2013). Utopian: User-driven topic modeling based on interactive nonnegative matrix factorization. IEEE Transactions on Visualization and Computer Graphics, 19(12), 1992–2001.
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Lake, J. A. (1994a). Reconstructing evolutionary trees from DNA and protein sequences: paralinear distances. Proceedings of the National Academy of Sciences, 91(4), 1455–1459.
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Lee, D. D., & Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755), 788–791.
Lee, D. D., & Seung, H. S. (2001). Algorithms for non-negative matrix factorization. In Advances in neural information processing systems (pp. 556–562).
Mei, Q., Cai, D., Zhang, D., & Zhai, C. (2008). Topic modeling with network regularization. In Proceedings of the 17th international conference on World Wide Web (pp. 101–110). ACM.
Song, H. A., & Lee, S.-Y. (2013). Hierarchical Representation Using NMF. In M. Lee, A. Hirose, Z.-G. Hou, & R. M. Kil (Eds.), Neural Information Processing: 20th International Conference, ICONIP 2013, Daegu, Korea, November 3-7, 2013. Proceedings, Part I (pp. 466–473). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-42054-2_58
Steyvers, M., & Griffiths, T. (2007). Probabilistic topic models. Handbook of Latent Semantic Analysis, 427(7), 424–440.
Xu, W., Liu, X., & Gong, Y. (2003). Document clustering based on non-negative matrix factorization. In Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval (pp. 267–273). ACM.
Zhu, H., Zhou, M., & Alkins, R. (2012). Group role assignment via a Kuhn–Munkres algorithm-based solution. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 42(3), 739–750.
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