  In this report, we describe our participant named-entity recognition system
at VLSP 2018 evaluation campaign. We formalized the task as a sequence labeling
problem using BIO encoding scheme. We applied a feature-based model which
combines word, word-shape features, Brown-cluster-based features, and
word-embedding-based features. We compare several methods to deal with nested
entities in the dataset. We showed that combining tags of entities at all
levels for training a sequence labeling model (joint-tag model) improved the
accuracy of nested named-entity recognition.
