Abstract
The Naive Mix is a new supervised learning algorithm that is based on a sequential method for selecting probabilistic models. The usual objective of model selection is to find a single model that adequately characterizes the data in a training sample. However, during model selection a sequence of models is generated that consists of the best-fitting model at each level of model complexity. The Naive Mix utilizes this sequence of models to define a probabilistic model which is then used as a probabilistic classifier to perform word-sense disambiguation. The models in this sequence are restricted to the class of decomposable log-linear models. This class of models offers a number of computational advantages. Experiments disambiguating twelve different words show that a Naive Mix formulated with a forward sequential search and Akaike's Information Criteria rivals established supervised learning algorithms such as decision trees (C4.5), rule induction (CN2) and nearest-neighbor classification (PEBLS).
Original language | English (US) |
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Title of host publication | Proceedings of the National Conference on Artificial Intelligence |
Editors | Anon |
Publisher | AAAI |
Pages | 604-609 |
Number of pages | 6 |
State | Published - Dec 1 1997 |
Event | Proceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97 - Providence, RI, USA Duration: Jul 27 1997 → Jul 31 1997 |
Other
Other | Proceedings of the 1997 14th National Conference on Artificial Intelligence, AAAI 97 |
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City | Providence, RI, USA |
Period | 7/27/97 → 7/31/97 |