Naive Mixes for Word Sense Disambiguation
Abstract
The Naive Mix is a new supervised learning algorithm based on sequential model selection. The usual objective of model selection is to find a single probabilistic model that adequately characterizes, i.e. fits, the data in a training sample. The Naive Mix combines models discarded during the selection process with the best--fitting model to form an averaged probabilistic model. This is shown to improve classification accuracy when applied to the problem of determining the meaning of an ambiguous word in a sentence.
Cite
Text
Pedersen. "Naive Mixes for Word Sense Disambiguation." AAAI Conference on Artificial Intelligence, 1997.Markdown
[Pedersen. "Naive Mixes for Word Sense Disambiguation." AAAI Conference on Artificial Intelligence, 1997.](https://mlanthology.org/aaai/1997/pedersen1997aaai-naive/)BibTeX
@inproceedings{pedersen1997aaai-naive,
title = {{Naive Mixes for Word Sense Disambiguation}},
author = {Pedersen, Ted},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1997},
pages = {841},
url = {https://mlanthology.org/aaai/1997/pedersen1997aaai-naive/}
}