Investigating GIS and smoothing for maximum entropy taggers (2003)
Cached
Download Links
- [www.cs.usyd.edu.au]
- [www.it.usyd.edu.au]
- [acl.ldc.upenn.edu]
- DBLP
Other Repositories/Bibliography
| Venue: | In Proceedings of the 10th Meeting of the EACL |
| Citations: | 38 - 8 self |
BibTeX
@INPROCEEDINGS{Curran03investigatinggis,
author = {James R. Curran and Stephen Clark},
title = {Investigating GIS and smoothing for maximum entropy taggers},
booktitle = {In Proceedings of the 10th Meeting of the EACL},
year = {2003},
pages = {91--98}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
This paper investigates two elements of Maximum Entropy tagging: the use of a correction feature in the Generalised Iterative Scaling (GIS) estimation algorithm, and techniques for model smoothing. We show analytically and empirically that the correction feature, assumed to be required for the correctness of GIS, is unnecessary. We also explore the use of a Gaussian prior and a simple cutoff for smoothing. The experiments are performed with two tagsets: the standard Penn Treebank POS tagset and the larger set of lexical types from Combinatory Categorial Grammar. 1
