Abstract
An implementation of a Spanish POS tagger is described in this paper. This implementation combines three basic approaches: a single word tagger based on decision trees, a POS tagger based on variable memory Markov models, and a feature structures set of tags. Using decision trees for single word tagging allows the tagger to work without a lexicon that lists only possible tags. Moreover, it decreases the error rate because there are no unknown words. The feature structure set of tags is advantageous when the available training corpus is small and the tag set large, which can be the case with morphologically rich languages like Spanish. Finally, variable memory Markov models training is more efficient than traditional full-order Markov models and achieves better accuracy. In this implementation, 98.58% of tokens are correctly classified.- Anthology ID:
- 2000.iwpt-1.25
- Volume:
- Proceedings of the Sixth International Workshop on Parsing Technologies
- Month:
- February 23-25
- Year:
- 2000
- Address:
- Trento, Italy
- Editors:
- Alberto Lavelli, John Carroll, Robert C. Berwick, Harry C. Bunt, Bob Carpenter, John Carroll, Ken Church, Mark Johnson, Aravind Joshi, Ronald Kaplan, Martin Kay, Bernard Lang, Alon Lavie, Anton Nijholt, Christer Samuelsson, Mark Steedman, Oliviero Stock, Hozumi Tanaka, Masaru Tomita, Hans Uszkoreit, K. Vijay-Shanker, David Weir, Mats Wiren
- Venue:
- IWPT
- SIG:
- SIGPARSE
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 254–265
- Language:
- URL:
- https://aclanthology.org/2000.iwpt-1.25
- DOI:
- Bibkey:
- Cite (ACL):
- José Triviño and Rafael Morales-Bueno. 2000. A Spanish POS Tagger with Variable Memory. In Proceedings of the Sixth International Workshop on Parsing Technologies, pages 254–265, Trento, Italy. Association for Computational Linguistics.
- Cite (Informal):
- A Spanish POS Tagger with Variable Memory (Triviño & Morales-Bueno, IWPT 2000)
- Copy Citation:
- PDF:
- https://aclanthology.org/2000.iwpt-1.25.pdf
Export citation
@inproceedings{trivino-morales-bueno-2000-spanish,
title = "A {S}panish {POS} Tagger with Variable Memory",
author = "Trivi{\~n}o, Jos{\'e} and
Morales-Bueno, Rafael",
editor = "Lavelli, Alberto and
Carroll, John and
Berwick, Robert C. and
Bunt, Harry C. and
Carpenter, Bob and
Carroll, John and
Church, Ken and
Johnson, Mark and
Joshi, Aravind and
Kaplan, Ronald and
Kay, Martin and
Lang, Bernard and
Lavie, Alon and
Nijholt, Anton and
Samuelsson, Christer and
Steedman, Mark and
Stock, Oliviero and
Tanaka, Hozumi and
Tomita, Masaru and
Uszkoreit, Hans and
Vijay-Shanker, K. and
Weir, David and
Wiren, Mats",
booktitle = "Proceedings of the Sixth International Workshop on Parsing Technologies",
month = feb # " 23-25",
year = "2000",
address = "Trento, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2000.iwpt-1.25",
pages = "254--265",
abstract = "An implementation of a Spanish POS tagger is described in this paper. This implementation combines three basic approaches: a single word tagger based on decision trees, a POS tagger based on variable memory Markov models, and a feature structures set of tags. Using decision trees for single word tagging allows the tagger to work without a lexicon that lists only possible tags. Moreover, it decreases the error rate because there are no unknown words. The feature structure set of tags is advantageous when the available training corpus is small and the tag set large, which can be the case with morphologically rich languages like Spanish. Finally, variable memory Markov models training is more efficient than traditional full-order Markov models and achieves better accuracy. In this implementation, 98.58{\%} of tokens are correctly classified.",
}
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<abstract>An implementation of a Spanish POS tagger is described in this paper. This implementation combines three basic approaches: a single word tagger based on decision trees, a POS tagger based on variable memory Markov models, and a feature structures set of tags. Using decision trees for single word tagging allows the tagger to work without a lexicon that lists only possible tags. Moreover, it decreases the error rate because there are no unknown words. The feature structure set of tags is advantageous when the available training corpus is small and the tag set large, which can be the case with morphologically rich languages like Spanish. Finally, variable memory Markov models training is more efficient than traditional full-order Markov models and achieves better accuracy. In this implementation, 98.58% of tokens are correctly classified.</abstract>
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%0 Conference Proceedings %T A Spanish POS Tagger with Variable Memory %A Triviño, José %A Morales-Bueno, Rafael %Y Lavelli, Alberto %Y Carroll, John %Y Berwick, Robert C. %Y Bunt, Harry C. %Y Carpenter, Bob %Y Church, Ken %Y Johnson, Mark %Y Joshi, Aravind %Y Kaplan, Ronald %Y Kay, Martin %Y Lang, Bernard %Y Lavie, Alon %Y Nijholt, Anton %Y Samuelsson, Christer %Y Steedman, Mark %Y Stock, Oliviero %Y Tanaka, Hozumi %Y Tomita, Masaru %Y Uszkoreit, Hans %Y Vijay-Shanker, K. %Y Weir, David %Y Wiren, Mats %S Proceedings of the Sixth International Workshop on Parsing Technologies %D 2000 %8 feb 23 25 %I Association for Computational Linguistics %C Trento, Italy %F trivino-morales-bueno-2000-spanish %X An implementation of a Spanish POS tagger is described in this paper. This implementation combines three basic approaches: a single word tagger based on decision trees, a POS tagger based on variable memory Markov models, and a feature structures set of tags. Using decision trees for single word tagging allows the tagger to work without a lexicon that lists only possible tags. Moreover, it decreases the error rate because there are no unknown words. The feature structure set of tags is advantageous when the available training corpus is small and the tag set large, which can be the case with morphologically rich languages like Spanish. Finally, variable memory Markov models training is more efficient than traditional full-order Markov models and achieves better accuracy. In this implementation, 98.58% of tokens are correctly classified. %U https://aclanthology.org/2000.iwpt-1.25 %P 254-265
Markdown (Informal)
[A Spanish POS Tagger with Variable Memory](https://aclanthology.org/2000.iwpt-1.25) (Triviño & Morales-Bueno, IWPT 2000)
- A Spanish POS Tagger with Variable Memory (Triviño & Morales-Bueno, IWPT 2000)
ACL
- José Triviño and Rafael Morales-Bueno. 2000. A Spanish POS Tagger with Variable Memory. In Proceedings of the Sixth International Workshop on Parsing Technologies, pages 254–265, Trento, Italy. Association for Computational Linguistics.