  In this paper, we present a feature-based named-entity recognition (NER)
model that achieves the start-of-the-art accuracy for Vietnamese language. We
combine word, word-shape features, PoS, chunk, Brown-cluster-based features,
and word-embedding-based features in the Conditional Random Fields (CRF) model.
We also explore the effects of word segmentation, PoS tagging, and chunking
results of many popular Vietnamese NLP toolkits on the accuracy of the proposed
feature-based NER model. Up to now, our work is the first work that
systematically performs an extrinsic evaluation of basic Vietnamese NLP
toolkits on the downstream NER task. Experimental results show that while
automatically-generated word segmentation is useful, PoS and chunking
information generated by Vietnamese NLP tools does not show their benefits for
the proposed feature-based NER model.
