Named Entity Recognition of Ancient Poems Based on Albert-BiLSTM-MHA-CRF Model

The extraction and construction of the knowledge graph related to the entity of ancient poems are helpful to excavate the connection between ancient poems, and it is of great significance to inherit the traditional Chinese culture. This paper proposes an Albert-BiLSTM-MHA-CRF model for entity extraction in ancient poems. Based on the BiLSTM-CRF model, the author introduces the Albert pretraining model and the multihead self-attention mechanism to extract character vectors and enhance the generalization ability of word embedding vectors and the potential semantics between characters of the model, depending on weight and other feature extraction capabilities. The experiment is carried out in the corpus of ancient poetry, and the model is compared with Bert-BiLSTM-CRF, BiLSTM-CRF, and CRF model. The results show that the entity extraction effect of ancient poetry is significantly improved, and the harmonic average value is 97.17%. Compared with Bert model as the pretraining model, Albert model reduces the time by 19.56%.