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基于主题嵌入的高效相关主题建模

Efficient Correlated Topic Modeling with Topic Embedding
课程网址: http://videolectures.net/kdd2017_zhang_topic_modeling/  
主讲教师: Muhan Zhang
开课单位: 视频讲座网
开课时间: 2017-10-09
课程语种: 英语
中文简介:
由于相关主题建模的计算成本高,可伸缩性差,一直局限于较小的模型和问题规模。在本文中,我们提出了一个新的模型,该模型学习紧凑主题嵌入,并通过主题向量之间的紧密度来捕获主题相关性。我们的方法能够在低维嵌入空间中进行有效的推理,将以前的三次或二次时间复杂度降低到主题大小的线性w.r.t。我们进一步用快速采样器加速变分推理,以利用主题出现的稀疏性。大量的实验表明,我们的方法能够处理比现有相关结果大几个数量级的模型和数据规模,而不牺牲建模质量,在文档分类和检索中提供具有竞争力或优越的性能。
课程简介: Correlated topic modeling has been limited to small model and problem sizes due to their high computational cost and poor scaling. In this paper, we propose a new model which learns compact topic embeddings and captures topic correlations through the closeness between the topic vectors. Our method enables efficient inference in the low-dimensional embedding space, reducing previous cubic or quadratic time complexity to linear w.r.t the topic size. We further speedup variational inference with a fast sampler to exploit sparsity of topic occurrence. Extensive experiments show that our approach is capable of handling model and data scales which are several orders of magnitude larger than existing correlation results, without sacrificing modeling quality by providing competitive or superior performance in document classification and retrieval.
关 键 词: 主题建模; 可伸缩性; 问题规模
课程来源: 视频讲座网
数据采集: 2022-12-16:chenxin01
最后编审: 2022-12-16:chenxin01
阅读次数: 16