End-to-End Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-Level Vs. Character-Level
نویسندگان
چکیده
This paper demonstrates end-to-end neural network architectures for Vietnamese named entity recognition. Our best model is the combination of bidirectional Long ShortTerm 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. Keywords-Vietnamese, named entity recognition, end-to-end
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