End-to-end Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-level vs. Character-level
Table 7: Comparison to participating NER systems at VLSP 2016
['[BOLD] Team', '[BOLD] Model', '[BOLD] Performance']
4.3.4 Comparison with Previous Systems In VLSP 2016 workshop, several different systems have been proposed for Vietnamese NER. In this campaign, they have evaluated over three entities types LOC, ORG, PER. In all fairness, we also evaluate our performances over these tags on the same training and test set. That system, however, used many hand-crafted features to improve the performance of maximum entropy classifier (ME) while our system is truly end-to-end model that takes only word sequences as inputs. Most approaches in VLSP 2016 used the CRF and ME models, whose performance is heavily dependent on feature engineering.