Name tagging with word clusters and discriminative training (2004)
| Venue: | Proceedings of HLT |
| Citations: | 39 - 0 self |
BibTeX
@INPROCEEDINGS{Miller04nametagging,
author = {Scott Miller and Jethran Guinness and Alex Zamanian},
title = {Name tagging with word clusters and discriminative training},
booktitle = {Proceedings of HLT},
year = {2004},
pages = {337--342}
}
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Abstract
We present a technique for augmenting annotated training data with hierarchical word clusters that are automatically derived from a large unannotated corpus. Cluster membership is encoded in features that are incorporated in a discriminatively trained tagging model. Active learning is used to select training examples. We evaluate the technique for named-entity tagging. Compared with a state-of-the-art HMM-based name finder, the presented technique requires only 13 % as much annotated data to achieve the same level of performance. Given a large annotated training set of 1,000,000 words, the technique achieves a 25 % reduction in error over the state-of-the-art HMM trained on the same material. 1
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