This paper presents a neural architecture for Vietnamese sequence labeling tasks including part-of-speech (POS) tagging and named entity recognition (NER). We applied the model described in \cite{lample-EtAl:2016:N16-1} that is a combination of bidirectional Long-Short Term Memory and Conditional Random Fields, which rely on two sources of information about words: character-based word representations learned from the supervised corpus and pre-trained word embeddings learned from other unannotated corpora. Experiments on benchmark datasets show that this work achieves state-of-the-art performances on both tasks - 93.52\% accuracy for POS tagging and 94.88\% F1 for NER. Our sourcecode is available at here.
@article{arxiv.1811.03754,
title = {Neural sequence labeling for Vietnamese POS Tagging and NER},
author = {Duong Nguyen Anh and Hieu Nguyen Kiem and Vi Ngo Van},
journal= {arXiv preprint arXiv:1811.03754},
year = {2018}
}