Attentive Neural Network for Named Entity Recognition in Vietnamese

Authors: Kim Anh Nguyen; Ngan Dong; Cam-Tu Nguyen

Abstract: We propose an attentive neural network for the task of named entity recognition in Vietnamese. The proposed attentive neural model makes use of character-based language models and word embeddings to encode words as vector representations. A neural network architecture of encoder, attention, and decoder layers is then utilized to encode knowledge of input sentences and to label entity tags. The experimental results show that the proposed attentive neural network achieves the state-of-the-art results on the benchmark named entity recognition datasets in Vietnamese in comparison to both hand-crafted features based models and neural models.

Publisher: IEEE

Published in: 2019 IEEE-RIVF International Conference on Computing and Communication Technologies (RIVF)

Date of Conference: 20-22 March 2019

DOI: 10.1109/RIVF.2019.8713740

Download: https://ieeexplore.ieee.org/document/8713740