Adaptive topic modeling with probabilistic pseudo feedback in online topic detection

Authors:
Guoyu Tang;Yunqing Xia
Affiliations:
Department of Computer Science and Technology, Tsinghua University, Beijing, China;Department of Computer Science and Technology, Tsinghua University, Beijing, China
Venue:
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Year:
2010

Citing 4
Cited 0

On-line new event detection and tracking

Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
TopCat: Data Mining for Topic Identification in a Text Corpus

PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Topic-conditioned novelty detection

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
NLP and IR approaches to monolingual and multilingual link detection

COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

Online topic detection (OTD) system seeks to analyze sequential stories in a real-time manner so as to detect new topics or to associate stories with certain existing topics. To handle new stories more precisely, an adaptive topic modeling method that incorporates probabilistic pseudo feedback is proposed in this paper to tune every topic model with a changed environment. Differently, this method considers every incoming story as pseudo feedback with certain probability, which is the similarity between the story and the topic. Experiment results show that probabilistic pseudo feedback brings promising improvement to online topic detection.