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Named entity recognition (NER) plays an important role in text-basedinformation retrieval. In this paper, we combine Bidirectional Long Short-TermMemory (Bi-LSTM) \cite{hochreiter1997,schuster1997} with Conditional RandomField (CRF) \cite{lafferty2001} to create a novel deep learning model for theNER problem. Each word as input of the deep learning model is represented by aWord2vec-trained vector. A word embedding set trained from about one millionarticles in 2018 collected through a Vietnamese news portal (baomoi.com). Inaddition, we concatenate a Word2Vec\cite{mikolov2013}-trained vector withsemantic feature vector (Part-Of-Speech (POS) tagging, chunk-tag) and hiddensyntactic feature vector (extracted by Bi-LSTM nerwork) to achieve the (so farbest) result in Vietnamese NER system. The result was conducted on the data setVLSP2016 (Vietnamese Language and Speech Processing 2016 \cite{vlsp2016})competition.

On the Vietnamese Name Entity Recognition: A Deep Learning Method Approach