BiC2V-NFLAT: Chinese toponym recognition based on Bi-Char2Vec and non-flat-lattice transformer

In response to the issues of missing context semantics and imbalanced datasets in Chinese toponym recognition tasks, this paper proposes a Chinese toponym recognition method based on Bi-Char2Vec and Non-Flat-Lattice Transformer. Initially, a new Chinese character embedding model, Bi-Char2Vec, is designed to capture the semantic representation of text in long sequences, mitigating the problem of missing context semantics in Chinese toponym recognition. Then, by utilizing Inter-Attention to interact between "character-word" attention, followed by encoding contextual information through the Self-Attention in Transformer; a global optimal tagging sequence is finally obtained by a Conditional Random Field layer. On public datasets, BIC2V-NFLAT improves the accuracy of Chinese toponym recognition, achieving an F1 score of 95%.