@inproceedings{45420a73170e4052a6da1a7fc83793b9,
title = "An improved Latent Dirichlet Allocation method for service topic detection",
abstract = "Service topic detection is one of the most important techniques in service information extraction, clustering and recommendation. Comparing with short text corpus in social network, service description corpus possesses higher dimensionality and more diversity. It is difficult to detect topics from a large number of service descriptions. To address these challenges, we proposed a new LDA (Latent Dirichlet Allocation) model based topic detection method, referred to as CV-LDA (Context sensitive word Vector based LDA). It utilizes a word embedding based method that generate context sensitive vector to cluster the words for decreasing dimensionality. Through topic perplexity analysis in the real-world dataset, it is obvious that topics detected by our method has a lower perplexity, comparing with word frequency weighing based vectors.",
keywords = "LDA Model, Perplexity, Service Topic, Word Embedding",
author = "Lantian Guo and Zhe Li and Tao Yang and Huixiang Zhang and Dejun Mu and Yang Li",
note = "Publisher Copyright: {\textcopyright} 2016 TCCT.; 35th Chinese Control Conference, CCC 2016 ; Conference date: 27-07-2016 Through 29-07-2016",
year = "2016",
month = aug,
day = "26",
doi = "10.1109/ChiCC.2016.7554469",
language = "英语",
series = "Chinese Control Conference, CCC",
publisher = "IEEE Computer Society",
pages = "7045--7049",
editor = "Jie Chen and Qianchuan Zhao and Jie Chen",
booktitle = "Proceedings of the 35th Chinese Control Conference, CCC 2016",
}