Modeling temporal text streams using the local multinomial model
Guy Lebanon, Yang Zhao, and Yanjun Zhao
Source: Electron. J. Statist. Volume 4
(2010), 566-584.
Abstract
Temporal text data such as news feeds cannot be adequately modeled by standard n-grams which correspond to multinomial or Markov chain models. Instead, we examine the application of local n-grams to modeling time stamped documents. We derive the asymptotic bias and variance and consider the bandwidth selection problem. Experimental results are presented on news feeds and web search query logs.
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Links and Identifiers
Permanent link to this document: http://projecteuclid.org/euclid.ejs/1276694115
Digital Object Identifier: doi:10.1214/09-EJS522
Mathematical Reviews number (MathSciNet): MR2660533
References
[1] Baeza-Yates, R. and Ribeiro-Neto, B. (1999)., Modern Information Retrieval. Addison Wesley.
[2] Jelinek, F. (1999)., Statistical methods for speech recognition. MIT press.
[3] Jurafsky, D., Martin, J. H. and Kehler, A. (2000)., Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition. MIT Press.
[4] Lewis, D., Yang, Y., Rose, T. and Li, F. (2004). RCV1: A new benchmark collection for text categorization research., Journal of Machine Learning Research 5 361–397.
[5] Manning, C. D. and Schutze, H. (1999)., Foundations of Statistical Natural Language Processing. MIT Press.
Mathematical Reviews (MathSciNet): MR1722790
Zentralblatt MATH: 0951.68158
[6] Pass, G., Chowdhury, A. and Torgeson, C. (2006). A picture of search. In, The First International Conference on Scalable Information Systems.
[7] Trujillo, A. (1999)., Translation engines: techniques for machine translation. Springer Verlag.
[8] Yang, Y. (1999). An evaluation of statistical approaches to text categorization., Information Retrieval 1 69–90.
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