From Word Segmentation to POS Tagging for Vietnamese
Abstract
This paper presents an empirical comparison of two strategies for Vietnamese Part-of-Speech (POS) tagging from unsegmented text: (i) a pipeline strategy where we consider the output of a word segmenter as the input of a POS tagger, and (ii) a joint strategy where we predict a combined segmentation and POS tag for each syllable. We also make a comparison between state-of-the-art (SOTA) feature-based and neural network-based models. On the benchmark Vietnamese treebank (Nguyen et al., 2009), experimental results show that the pipeline strategy produces better scores of POS tagging from unsegmented text than the joint strategy, and the highest accuracy is obtained by using a feature-based model.
Keywords
Cite
@article{arxiv.1711.04951,
title = {From Word Segmentation to POS Tagging for Vietnamese},
author = {Dat Quoc Nguyen and Thanh Vu and Dai Quoc Nguyen and Mark Dras and Mark Johnson},
journal= {arXiv preprint arXiv:1711.04951},
year = {2017}
}
Comments
To appear in Proceedings of the 15th Annual Workshop of the Australasian Language Technology Association, ALTA 2017