Research on Korean Named Entity Recognition Based on Syllable-Morpheme Fusion

The named entity recognition task is one of the most important fundamental tasks in the process of Korean natural language processing research. In order to deal with the problems of unclear boundary delimitation and low accuracy rate of Korean named entity recognition, this paper proposes a Korean syllable word fusion named entity recognition model based on Transformer's. Firstly, word embedding is performed for syllables and morphemes separately by the BERT pre-training model. And secondly, two different vector fusion methods are used to fuse syllable vectors and word vectors, i.e., a simple vector splicing method and a heuristic fusion method that takes into account the connection and difference between the two vectors, and the fused vectors are used as the input of the SWT-NER model. As well the F1 value in the Korean named entity recognition dataset published by KLUE reached 88.03%, which is about 3 to 4 percentage points higher than the single granularity experiment.