End-to-end Recurrent Neural Network Models for Vietnamese Named Entity Recognition: Word-level vs. Character-level
Figure 4: Word and character-level sequence labeling of the sentence Anh rời EU hôm_qua. (UK left EU yesterday.)
['A G', 'n G', 'h G', 'O', 'r O', 'ờ O', 'i O', 'O', 'E G', 'U G', 'O', 'h O', 'ô O', 'm O', '_ O', 'q O', 'u O', 'a O']
The training data for NER is in CoNLL-2003 format, where both input and output sequence are annotated at word-level. For this reason, it is necessary to convert the dataset from word-level sequences to character-level sequences. We use a simple method in which all characters of a word are labeled with the same tag. For example, the label of all characters of a person named entity is P. Similarly, all characters of location, organization, and miscellaneous tokens are labelled with letters L, G, and M respectively. Anh rời EU hôm qua (UK left EU yesterday) and