BTM: Topic Modeling over Short Texts
BTM: Topic Modeling over Short Texts
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BTM：短文本主题建模
DOI：
10.1109/tkde.2014.2313872
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发表时间：
2014-12-01
影响因子：
8.9
通讯作者：
Guo, Jiafeng
中科院分区：
文献类型：
--
作者：
Cheng, Xueqi;Yan, Xiaohui;Guo, Jiafeng
短文本在当今的网络上很受欢迎，尤其是随着社交媒体的出现。对许多内容分析任务来说，从大规模短文本中推断主题是一项关键而又具有挑战性的任务。传统的主题模型，如潜在狄利let分配（LDA）和概率潜在语义分析（PLSA），通过将每个文档建模为主题的混合，从文档级单词共现中学习主题，其推理受到短文本中单词共现模式的稀疏性的影响。本文提出了一种新的短文本主题建模方法——双词主题模型（BTM）。BTM通过对语料库中词共现模式（即双词）的生成直接建模来学习主题，利用丰富的语料库级信息进行有效的推理。为了处理大规模的短文本数据，我们进一步引入了两种在线的BTM算法来实现高效的主题学习。在真实单词短文本集合上的实验表明，BTM可以发现更突出和连贯的主题，并且显著优于最先进的基线。我们还展示了两种在线BTM算法在时间效率和主题学习方面的令人满意的性能。
Short texts are popular on today's web, especially with the emergence of social media. Inferring topics from large scale short texts becomes a critical but challenging task for many content analysis tasks. Conventional topic models such as latent Dirichlet allocation (LDA) and probabilistic latent semantic analysis (PLSA) learn topics from document-level word co-occurrences by modeling each document as a mixture of topics, whose inference suffers from the sparsity of word co-occurrence patterns in short texts. In this paper, we propose a novel way for short text topic modeling, referred as biterm topic model (BTM). BTM learns topics by directly modeling the generation of word co-occurrence patterns (i.e., biterms) in the corpus, making the inference effective with the rich corpus-level information. To cope with large scale short text data, we further introduce two online algorithms for BTM for efficient topic learning. Experiments on real-word short text collections show that BTM can discover more prominent and coherent topics, and significantly outperform the state-of-the-art baselines. We also demonstrate the appealing performance of the two online BTM algorithms on both time efficiency and topic learning.