Joint Extraction of Entities and Relations for Chinese Text of Tea

In view of the problems of polysemy and overlapping relations of Chinese tea text. In this paper, we present a joint model BERT-LCM-Tea for extraction of entities and relations, which combines the Bidirectional Encoder Representations from Transformers (BERT) and the last character matching (LCM) algorithm. This model uses BERT to fine-tuning character embedding through contextual information, the problem of polysemy is solved and the performance of entity recognition of Chinese tea text is improved. In addition, the model uses last character matching algorithm, the problem of overlapping relations is solved and the accuracy of relation extraction of Chinese tea text is improved. The experimental results show that BERT-LCM-Tea F1 score to 86.8% in entity recognition task and F1 score to 77.1% in relation extraction task, which is higher than the currently popular Bi-RNN-CRF, Bi-LSTM-CRF and Bi-GRU-CRF. Thus, the BERT-LCM-Tea is more suitable for the entity recognition and relation extraction of Chinese tea text, and provides a basis for future research on the construction of tea knowledge graph.