Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
SALSA: the stochastic approach for link-structure analysis
ACM Transactions on Information Systems (TOIS)
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Looking for lumps: boosting and bagging for density estimation
Computational Statistics & Data Analysis - Nonlinear methods and data mining
RCV1: A New Benchmark Collection for Text Categorization Research
The Journal of Machine Learning Research
A graph-theoretic approach to extract storylines from search results
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Incremental probabilistic latent semantic analysis for automatic question recommendation
Proceedings of the 2008 ACM conference on Recommender systems
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In this paper we introduce a novel approach for incrementally building aspect models, and use it to dynamically discover underlying themes from document streams. Using the new approach we present an application which we call “query-line tracking” i.e., we automatically discover and summarize different themes or stories that appear over time, and that relate to a particular query. We present evaluation on news corpora to demonstrate the strength of our method for both query-line tracking, online indexing and clustering.