Attentive Neural Network for Named Entity Recognition in Vietnamese
This work addresses a domain-specific problem for Vietnamese language processing, representing an incremental improvement over prior methods.
The authors tackled named entity recognition in Vietnamese by proposing an attentive neural network, achieving state-of-the-art results on benchmark datasets compared to existing models.
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.