@inproceedings{f594b845e7bd4bdcb53cb79d41d88c1e,
title = "An online inference algorithm for Labeled Latent Dirichlet allocation",
abstract = "Using topic models to analyze documents is a popular method in text mining. Labeled Latent Dirichlet Allocation(Labeled LDA) is one of them that is widely used to model tagged documents and to solve relevant problems, such as tagged document visualization, snippet extraction and so on. However, traditional batch inference for Labeled LDA, which runs over entire document collection, is computationally expensive and not suitable for large scale corpora and text streams. In this paper, we develop an efficient online algorithm for Labeled LDA, called online Labeled LDA(online-LLDA). It is based on particle filter, a Sequential Monte Carlo approximation technique. Our experiments show that online-LLDA significantly outperforms batch algorithm(batch- LLDA) in time, while preserving equivalent quality.",
keywords = "Online inference, Online labeled lda, Particle filter",
author = "Qiang Zhou and Heyan Huang and Mao, \{Xian Ling\}",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 17th Asia-PacificWeb Conference, APWeb 2015 ; Conference date: 18-09-2015 Through 20-09-2015",
year = "2015",
doi = "10.1007/978-3-319-25255-1\_2",
language = "English",
isbn = "9783319252544",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "17--28",
editor = "Reynold Cheng and Bin Cui and Zhenjie Zhang and Ruichu Cai and Jia Xu",
booktitle = "Web Technologies and Applications - 17th Asia-PacificWeb Conference,APWeb 2015, Proceedings",
address = "Germany",
}