Memory-Based Learning of Morphology with Stochastic Transducers (2002)
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| Venue: | In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL |
| Citations: | 13 - 2 self |
BibTeX
@INPROCEEDINGS{Clark02memory-basedlearning,
author = {Alexander Clark and Issco Tim},
title = {Memory-Based Learning of Morphology with Stochastic Transducers},
booktitle = {In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL},
year = {2002},
pages = {513--520}
}
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Abstract
This paper discusses the supervised learning of morphology using stochastic transducers, trained using the ExpectationMaximization (EM) algorithm. Two approaches are presented: first, using the transducers directly to model the process, and secondly using them to define a similarity measure, related to the Fisher kernel method (Jaakkola and Haussler, 1998), and then using a Memory-Based Learning (MBL) technique. These are evaluated and compared on data sets from English, German, Slovene and Arabic.
Citations
| 270 | Statistical inference for probabilistic functions of finite state Markov chains - Baum, Petrie - 1966 |
| 31 | Finite-state models in the alignment of macro-molecules - Allison, Wallace, et al. - 1992 |
| 2 | Casacuberta and Colin de la Higuera. 2000. Computational complexity of problems on probabilistic grammars and transducers - Francisco |
