A manually annotated Vietnamese COVID-19 NER dataset with 11 entity types and up to four nesting levels, plus BiLSTM and PhoBERT baselines where PhoBERT-large-CRF with cross-sentence context achieved the highest F1.
A Fast and Accurate Vietnamese Word Segmenter
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We propose a novel approach to Vietnamese word segmentation. Our approach is based on the Single Classification Ripple Down Rules methodology (Compton and Jansen, 1990), where rules are stored in an exception structure and new rules are only added to correct segmentation errors given by existing rules. Experimental results on the benchmark Vietnamese treebank show that our approach outperforms previous state-of-the-art approaches JVnSegmenter, vnTokenizer, DongDu and UETsegmenter in terms of both accuracy and performance speed. Our code is open-source and available at: https://github.com/datquocnguyen/RDRsegmenter.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Nested Named-Entity Recognition on Vietnamese COVID-19: Dataset and Experiments
A manually annotated Vietnamese COVID-19 NER dataset with 11 entity types and up to four nesting levels, plus BiLSTM and PhoBERT baselines where PhoBERT-large-CRF with cross-sentence context achieved the highest F1.