Integrating topics and syntax. Blei, D. M, Griffiths, T. L, Jordan, M. I, & Tenenbaum, J. B In Advances in Neural Information Processing Systems 17. MIT Press, Cambridge, MA, 2004.
abstract   bibtex   
Statistical approaches to language learning typically focus on either short-range syntactic dependencies or long-range semantic dependencies between words. We present a generative model that uses both kinds of dependencies, and is capable of simultaneously finding syntactic classes and semantic topics despite having no knowledge of syntax or semantics beyond statistical dependency. This model is competitive on tasks like part-of-speech tagging and document classification with models that exclusively use short- and long-range dependencies respectively.
@incollection{Blei/etal:04,
	address = {Cambridge, MA},
	title = {Integrating topics and syntax},
	abstract = {Statistical approaches to language learning typically focus on either
short-range syntactic dependencies or long-range semantic dependencies
between words. We present a generative model that uses both kinds of
dependencies, and is capable of simultaneously finding syntactic classes
and semantic topics despite having no knowledge of syntax or semantics
beyond statistical dependency. This model is competitive on tasks
like part-of-speech tagging and document classification with models that
exclusively use short- and long-range dependencies respectively.},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 17},
	publisher = {MIT Press},
	author = {Blei, David M and Griffiths, Thomas L and Jordan, Michael I and Tenenbaum, Joshua B},
	year = {2004},
}

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