| Presentation | 2010-06-14 Hierarchical Pitman-Yor Topic Model Issei SATO, Hiroshi NAKAGAWA, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Topic model is a probabilistic generative model that models latent semantics of words as "topic". In this paper, we propose a novel topic model that captures the power-law phenomenon of a word distribution, which is known as Zipf's law in linguistics. We use the Pitman-Yor process to model a generation process of a document. In an experiment using real data, our model outperformed LDA in document modeling in terms of perplexity. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Topic model / Pitman-Yor process |
| Paper # | IBISML2010-7 |
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| Conference Information | |
| Committee | IBISML |
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| Conference Date | 2010/6/7(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Hierarchical Pitman-Yor Topic Model |
| Sub Title (in English) | |
| Keyword(1) | Topic model |
| Keyword(2) | Pitman-Yor process |
| 1st Author's Name | Issei SATO |
| 1st Author's Affiliation | Graduate School of Information Sience and Technology, The University of Tokyo() |
| 2nd Author's Name | Hiroshi NAKAGAWA |
| 2nd Author's Affiliation | Interfaculty Initiative in Information Studies |
| Date | 2010-06-14 |
| Paper # | IBISML2010-7 |
| Volume (vol) | vol.110 |
| Number (no) | 76 |
| Page | pp.pp.- |
| #Pages | 7 |
| Date of Issue | |