1 research outputs found
Attentive Neural Network for Named Entity Recognition in Vietnamese
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