Title: End-to-end Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-level vs. Character-level,
Abstract: This paper demonstrates end-to-end neural network architectures for
Vietnamese named entity recognition. Our best model is a combination of
bidirectional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network
(CNN), Conditional Random Field (CRF), using pre-trained word embeddings as
input, which achieves an F1 score of 88.59% on a standard test set. Our system
is able to achieve a comparable performance to the first-rank system of the
VLSP campaign without using any syntactic or hand-crafted features. We also
give an extensive empirical study on using common deep learning models for
Vietnamese NER, at both word and character level.