Neural sequence labeling for Vietnamese POS Tagging and NER
This work addresses sequence labeling for Vietnamese language processing, but it is incremental as it applies an existing method to a specific domain.
The paper tackled Vietnamese part-of-speech tagging and named entity recognition using a neural sequence labeling model, achieving state-of-the-art results with 93.52% accuracy for POS tagging and 94.88% F1 for NER.
This paper presents a neural architecture for Vietnamese sequence labeling tasks including part-of-speech (POS) tagging and named entity recognition (NER). We applied the model described in \cite{lample-EtAl:2016:N16-1} that is a combination of bidirectional Long-Short Term Memory and Conditional Random Fields, which rely on two sources of information about words: character-based word representations learned from the supervised corpus and pre-trained word embeddings learned from other unannotated corpora. Experiments on benchmark datasets show that this work achieves state-of-the-art performances on both tasks - 93.52\% accuracy for POS tagging and 94.88\% F1 for NER. Our sourcecode is available at here.