BERT-based Named Entity Recognition Method for Chinese Recipe Text

In order to extract the information from the massive unstructured recipe text, a named entity recognition method based on BERT pre-trained language model is proposed by this paper. Firstly, after pre-processing the public recipe texts obtained from the web, the entities were classified into three categories of main ingredients, auxiliary ingredients and cooking methods by combining the domain expert knowledge and BIO annotation method to build a large-scale Chinese cuisine domain named entity recognition dataset, and then the BERT-Attention-BiLSTM-CRF(BABC) model proposed in this paper is used to train on this dataset. Finally, we compared the results of other named entity recognition models on this dataset, and the experimental results showed that BABC could identify the entities in the recipe text more accurately.