Abstract
NER is a basic research in the field of NLP. Most of the Chinese NER tasks are for the recognition of names of people, places, and institutions. The recognized content is single and cannot be applied in many fields. In this paper, we label and build the NER dataset for the steel field. Based on the BiLSTM-CRF model, we proposed character and word combination embedding method. Experiments show that our method reduces the OOV problem, which has an increase in 7.20% and 5.03% in the F1 score compared with the traditional character embedding and word embedding.
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