TY - GEN
T1 - Distributed latent variable models of lexical co-occurrences
AU - Blitzer, John
AU - Globerson, Amir
AU - Pereira, Fernando
PY - 2005
Y1 - 2005
N2 - Low-dimensional representations for lexical co-occurrence data have become increasingly important in alleviating the sparse data problem inherent in natural language processing tasks. This work presents a distributed latent variable model for inducing these low-dimensional representations. The model takes inspiration from both connectionist language models [1, 16] and latent variable models [13, 9]. We give results which show that the new model significantly improves both bigram and trigram models.
AB - Low-dimensional representations for lexical co-occurrence data have become increasingly important in alleviating the sparse data problem inherent in natural language processing tasks. This work presents a distributed latent variable model for inducing these low-dimensional representations. The model takes inspiration from both connectionist language models [1, 16] and latent variable models [13, 9]. We give results which show that the new model significantly improves both bigram and trigram models.
UR - https://www.scopus.com/pages/publications/84862620701
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AN - SCOPUS:84862620701
SN - 097273581X
SN - 9780972735810
T3 - AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics
SP - 25
EP - 32
BT - AISTATS 2005 - Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics
PB - The Society for Artificial Intelligence and Statistics
T2 - 10th International Workshop on Artificial Intelligence and Statistics, AISTATS 2005
Y2 - 6 January 2005 through 8 January 2005
ER -