zh:courses:ml2025:ch05
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zh:courses:ml2025:ch05 [2025/12/17 22:10] pzczxs 创建 |
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| - | ====== 概率主题模型 ====== | + | ====== 第五章：概率主题模型 ====== |
| ====课件==== | ====课件==== | ||
| - | 下载：概率主题模型【PDF，PPT】 | + | 下载：概率主题模型【{{ :zh:courses:ml2025:ch05.pdf |PDF}}，{{ :intranet:courses:ml2025:ch05.pptx |PPT}}】 |
| ====推荐读物==== | ====推荐读物==== | ||
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| -张晗, 徐硕, 乔晓东, 2014. [[http://d.wanfangdata.com.cn/Periodical_qbxb201410013.aspx|融合科技文献内外部特征的主题模型发展综述]]. //情报学报//, Vol. 33, No. 10, pp. 1108-1120. | -张晗, 徐硕, 乔晓东, 2014. [[http://d.wanfangdata.com.cn/Periodical_qbxb201410013.aspx|融合科技文献内外部特征的主题模型发展综述]]. //情报学报//, Vol. 33, No. 10, pp. 1108-1120. | ||
| -Xuerui Wang, Andrew McCallum, and Xing Wei, 2007. [[https://doi.org/10.1109/ICDM.2007.86|Topical N-grams: Phrase and Topic Discovery, with an Application to Information Retrieval]]. //ICDM//, 697-702. | -Xuerui Wang, Andrew McCallum, and Xing Wei, 2007. [[https://doi.org/10.1109/ICDM.2007.86|Topical N-grams: Phrase and Topic Discovery, with an Application to Information Retrieval]]. //ICDM//, 697-702. | ||
| + | -Rajarshi Das, Manzil Zaheer, and Chris Dyer, 2015. [[https://doi.org/10.3115/v1/P15-1077|Gaussian LDA for Topic Models with Word Embeddings]]. //Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing//, pp. 795-804. | ||
| -Shuo Xu, Liyuan Hao, Guancan Yang, Kun Lu, and Xin An, 2021. [[https://doi.org/10.1016/j.techfore.2020.120366|A Topic Models based Framework for Detecting and Forecasting Emerging Technologies]]. //Technology Forecasting and Social Change//, Vol. 162, pp. 120366. [[:zh:notes:techemergence|Note]] | -Shuo Xu, Liyuan Hao, Guancan Yang, Kun Lu, and Xin An, 2021. [[https://doi.org/10.1016/j.techfore.2020.120366|A Topic Models based Framework for Detecting and Forecasting Emerging Technologies]]. //Technology Forecasting and Social Change//, Vol. 162, pp. 120366. [[:zh:notes:techemergence|Note]] | ||
| -Zheng Wang, Shuo Xu, and Lijun Zhu, 2018. [[https://doi.org/10.1016/j.jbi.2018.08.011|Semantic Relation Extraction Aware of N-Gram Features from Unstructured Biomedical Text]]. //Journal of Biomedical Informatics//, Vol. 86, pp. 59-70. [[:zh:notes:rel_type_tng|Note]] | -Zheng Wang, Shuo Xu, and Lijun Zhu, 2018. [[https://doi.org/10.1016/j.jbi.2018.08.011|Semantic Relation Extraction Aware of N-Gram Features from Unstructured Biomedical Text]]. //Journal of Biomedical Informatics//, Vol. 86, pp. 59-70. [[:zh:notes:rel_type_tng|Note]] | ||
zh/courses/ml2025/ch05.1765980602.txt.gz · 最后更改: 2025/12/17 22:10 由 pzczxs