Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval
Abstract
Many problems in natural language learn-ing and information retrieval involve estimat-ing probabilities in very large discrete state spaces. Dimension reduction as well as clus-tering techniques in various \navors have been popular choices to deal with the problem of data sparseness. In this paper, we present a general framework for dimension reduc-tion based on curved multinomial subfami-lies. The investigated class of models include dierent geometries as well as various objec-tive functions and algorithms for model t-ting. The pursued goal is twofold { to achieve a systematic understanding of the dierences and similarities between various models and to empirically investigate their generalization performance on a number of representative data sets. 1.
Cite
Text
Hall and Hofmann. "Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval." International Conference on Machine Learning, 2000.Markdown
[Hall and Hofmann. "Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval." International Conference on Machine Learning, 2000.](https://mlanthology.org/icml/2000/hall2000icml-learning/)BibTeX
@inproceedings{hall2000icml-learning,
title = {{Learning Curved Multinomial Subfamilies for Natural Language Processing and Information Retrieval}},
author = {Hall, Keith B. and Hofmann, Thomas},
booktitle = {International Conference on Machine Learning},
year = {2000},
pages = {351-358},
url = {https://mlanthology.org/icml/2000/hall2000icml-learning/}
}