A Non-Parametric Topic Model for Short Texts Incorporating Word Coherence Knowledge

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	A Non-Parametric Topic Model for Short Texts Incorporating Word Coherence Knowledge
	Yuhao Zhang1 ; Wenji Mao1,2 ; Daniel Zeng1,2
	2016
会议名称	The 2016 ACM International Conference on Information and Knowledge Management
会议日期	October 23-28, 2016
会议地点	Indianapolis, USA
摘要	Mining topics in short texts (e.g. tweets, instant messages) can help people grasp essential information and understand key contents, and is widely used in many applications related to social media and text analysis. The sparsity and noise of short texts often restrict the performance of traditional topic models like LDA. Recently proposed Biterm Topic Model (BTM) which models word co-occurrence patterns directly, is revealed effective for topic detection in short texts. However, BTM has two main drawbacks. It needs to manually specify topic number, which is difficult to accurately determine when facing new corpora. Besides, BTM assumes that two words in same term should belong to the same topic, which is often too strong as it does not differentiate two types of words (i.e. general words and topical words). To tackle these problems, in this paper, we propose a nonparametric topic model npCTM with the above distinction. Our model incorporates the Chinese restaurant process (CRP) into the BTM model to determine topic number automatically. Our model also distinguishes general words from topical words by jointly considering the distribution of these two word types for each word as well as word coherence information as prior knowledge. We carry out experimental studies on real-world twitter dataset. The results demonstrate the effectiveness of our method to discover coherent topics compared with the baseline methods.
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/14510
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
作者单位	1.Institute of Automation, Chinese Academy of Sciences 2.School of Computer and Control Engineering, University of Chinese Academy of Sciences
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Yuhao Zhang,Wenji Mao,Daniel Zeng. A Non-Parametric Topic Model for Short Texts Incorporating Word Coherence Knowledge[C],2016.

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