Part of Speech Tagging: Shallow or Deep Learning?
Deep neural networks have advanced the state of the art in numerous fields, but they generally suffer from low computational efficiency and the level of improvement compared to more efficient machine learning models is not always significant. We perform a thorough PoS tagging evaluation on the Univ...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Linköping University Electronic Press
2018-06-01
|
| Series: | Northern European Journal of Language Technology |
| Subjects: | |
| Online Access: | https://nejlt.ep.liu.se/article/view/218 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Deep neural networks have advanced the state of the art in numerous fields, but they generally suffer from low computational efficiency and the level of improvement compared to more efficient machine learning models is not always significant. We perform a thorough PoS tagging evaluation on the Universal Dependencies treebanks, pitting a state-of-the-art neural network approach against UDPipe and our sparse structured perceptron-based tagger, efselab. In terms of computational efficiency, efselab is three orders of magnitude faster than the neural network model, while being more accurate than either of the other systems on 47 of 65 treebanks.
|
|---|---|
| ISSN: | 2000-1533 |