Local likelihood modeling of temporal text streams

Guy Lebanon, Yang Zhao
2008 Proceedings of the 25th international conference on Machine learning - ICML '08  
Temporal text data is often generated by a time-changing process or distribution. Such a drift in the underlying distribution cannot be captured by stationary likelihood techniques. We consider the application of local likelihood methods to generative and conditional modeling of temporal document sequences. We examine the asymptotic bias and variance and present an experimental study using the RCV1 dataset containing a temporal sequence of Reuters news stories.
doi:10.1145/1390156.1390226 dblp:conf/icml/LebanonZ08 fatcat:djqugtg3tzgrrcxzuowf43si2y