Tourism named entity recognition method based on knowledge enhancement

Aiming at the problems of multiple meanings, long lengths, and close connections with context information in the recognition of named entities in the field of Chinese tourism, as well as the complex composition of some entities, this paper proposes knowledge-enhanced tourism naming entity recognition method. Firstly, the knowledge-enhanced ERNIE pre-trained language model is utilized to obtain the semantic representation of tourism text. Secondly, the obtained word vectors perform feature learning and feature representation of local information on the input data through Convolutional Neural Networks (CNNs). Then, the Bi-directional Long Short-Term Memory (BiLSTM) is used to fully learn the forward and backward feature information of tourism text. At last, the tag decoding layer based on Conditional Random Fields (CRFs) is employed to address tag dependencies and produce the optimal sequence of tourism named entity tags. The effectiveness of the ERNIE-CNN-BiLSTM-CRF model is confirmed by experimental results, which involve a comparison with other models using the created tourism dataset.