The Importance of Automatic Syntactic Features in Vietnamese Named Entity Recognition
This improves NER for Vietnamese language processing, but it is incremental as it builds on existing methods.
The paper tackled Vietnamese Named Entity Recognition by incorporating automatic syntactic features with word embeddings into a Bi-LSTM system, achieving an overall F1 score of 92.05% on a test set and outperforming previous systems by a large margin.
This paper presents a state-of-the-art system for Vietnamese Named Entity Recognition (NER). By incorporating automatic syntactic features with word embeddings as input for bidirectional Long Short-Term Memory (Bi-LSTM), our system, although simpler than some deep learning architectures, achieves a much better result for Vietnamese NER. The proposed method achieves an overall F1 score of 92.05% on the test set of an evaluation campaign, organized in late 2016 by the Vietnamese Language and Speech Processing (VLSP) community. Our named entity recognition system outperforms the best previous systems for Vietnamese NER by a large margin.