摘要
In the text literature, many topic models were proposed to represent documents and words as topics or latent topics in order to process text effectively and accurately. In this paper, we propose LDACLM or Latent Dirichlet Allocation Category Language Model for text categorization and estimate parameters of models by variational inference. As a variant of Latent Dirichlet Allocation Model, LDACLM regard documents of category as Language Model and use variational parameters to estimate maximum a posteriori of terms. Experiments show LDACLM model to be effective for text categorization, outperforming standard Naive Bayes and Rocchio method for text categorization.
| 源语言 | 英语 |
|---|---|
| 主期刊名 | Rough Sets and Knowledge Technology - Third International Conference, RSKT 2008, Proceedings |
| 页 | 572-579 |
| 页数 | 8 |
| DOI | |
| 出版状态 | 已出版 - 2008 |
| 活动 | 3rd International Conference on Rough Sets and Knowledge Technology, RSKT 2008 - Chengdu, 中国 期限: 17 5月 2008 → 19 5月 2008 |
出版系列
| 姓名 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| 卷 | 5009 LNAI |
| ISSN（印刷版） | 0302-9743 |
| ISSN（电子版） | 1611-3349 |
会议
| 会议 | 3rd International Conference on Rough Sets and Knowledge Technology, RSKT 2008 |
|---|---|
| 国家/地区 | 中国 |
| 市 | Chengdu |
| 时期 | 17/05/08 → 19/05/08 |
指纹
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Zhou, S., Li, K., & Liu, Y. (2008). Text categorization based on topic model. 在 Rough Sets and Knowledge Technology - Third International Conference, RSKT 2008, Proceedings (页码 572-579). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 5009 LNAI). https://doi.org/10.1007/978-3-540-79721-0_77