Topic-Based Language Models Using EM (1999)
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| Venue: | IN PROCEEDINGS OF EUROSPEECH |
| Citations: | 35 - 1 self |
BibTeX
@INPROCEEDINGS{Gildea99topic-basedlanguage,
author = {Daniel Gildea and Thomas Hofmann},
title = {Topic-Based Language Models Using EM},
booktitle = {IN PROCEEDINGS OF EUROSPEECH},
year = {1999},
pages = {2167--2170},
publisher = {}
}
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Abstract
In this paper, we propose a novel statistical language model to capture topic-related long-range dependencies. Topics are modeled in a latent variable framework in which we also derive an EM algorithm to perform a topic factor decomposition based on a segmented training corpus. The topic model is combined with a standard language model to be used for on-line word prediction. Perplexity results indicate an improvement over previously proposed topic models, which unfortunately has not translated into lower word error.
