图 书 馆
动态环境下的双马尔可夫链主题模型A Dual Markov Chain Topic Model for Dynamic Environments |
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| 课程网址: | http://videolectures.net/kdd2018_acharya_markov_environments/ |
| 主讲教师: | Ayan Acharya |
| 开课单位: | 德克萨斯大学奥斯汀分校 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The abundance of digital text has led to extensive research on topic models that reason about documents using latent representations. Since for many online or streaming textual sources such as news outlets, the number, and nature of topics change over time, there have been several efforts that attempt to address such situations using dynamic versions of topic models. Unfortunately, existing approaches encounter more complex inferencing when their model parameters are varied over time, resulting in high computation complexity and performance degradation. This paper introduces the DM-DTM, a dual Markov chain dynamic topic model, for characterizing a corpus that evolves over time. This model uses a gamma Markov chain and a Dirichlet Markov chain to allow the topic popularities and word-topic assignments, respectively, to vary smoothly over time. Novel applications of the NegativeBinomial augmentation trick result in simple, efficient, closed-form updates of all the required conditional posteriors, resulting in far lower computational requirements as well as less sensitivity to initial conditions, as compared to existing approaches. Moreover, via a gamma process prior, the number of desired topics is inferred directly from the data rather than being pre-specified and can vary as the data changes. Empirical comparisons using multiple realworld corpora demonstrate a clear superiority of DM-DTM over strong baselines for both static and dynamic topic models. |
| 关 键 词: | 双马尔可夫链主题模型; 负细胞扩增技巧; Dirichlet马尔可夫链; 多个现实世界语料库 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-09：cyh |
| 最后编审: | 2023-05-15：cyh |
| 阅读次数: | 32 |