  Named entity recognition (NER) plays an important role in text-based
information retrieval. In this paper, we combine Bidirectional Long Short-Term
Memory (Bi-LSTM) \cite{hochreiter1997,schuster1997} with Conditional Random
Field (CRF) \cite{lafferty2001} to create a novel deep learning model for the
NER problem. Each word as input of the deep learning model is represented by a
Word2vec-trained vector. A word embedding set trained from about one million
articles in 2018 collected through a Vietnamese news portal (baomoi.com). In
addition, we concatenate a Word2Vec\cite{mikolov2013}-trained vector with
semantic feature vector (Part-Of-Speech (POS) tagging, chunk-tag) and hidden
syntactic feature vector (extracted by Bi-LSTM nerwork) to achieve the (so far
best) result in Vietnamese NER system. The result was conducted on the data set
VLSP2016 (Vietnamese Language and Speech Processing 2016 \cite{vlsp2016})
competition.
