Shared Context Probabilistic Transducers
Abstract
Recently, a model for supervised learning of probabilistic transduc(cid:173) ers represented by suffix trees was introduced. However, this algo(cid:173) rithm tends to build very large trees, requiring very large amounts of computer memory. In this paper, we propose anew, more com(cid:173) pact, transducer model in which one shares the parameters of distri(cid:173) butions associated to contexts yielding similar conditional output distributions . We illustrate the advantages of the proposed algo(cid:173) rithm with comparative experiments on inducing a noun phrase recogmzer.
Cite
Text
Bengio et al. "Shared Context Probabilistic Transducers." Neural Information Processing Systems, 1997.Markdown
[Bengio et al. "Shared Context Probabilistic Transducers." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/bengio1997neurips-shared/)BibTeX
@inproceedings{bengio1997neurips-shared,
title = {{Shared Context Probabilistic Transducers}},
author = {Bengio, Yoshua and Bengio, Samy and Isabelle, Jean-Franc and Singer, Yoram},
booktitle = {Neural Information Processing Systems},
year = {1997},
pages = {409-415},
url = {https://mlanthology.org/neurips/1997/bengio1997neurips-shared/}
}