关键词: 谷物收割机械, 维修, 命名实体识别, 注意力机制, 广义自回归预训练语言模型（XLNet）

Abstract: Aiming at the problems of lack of context semantic features, insufficient long‑distance dependence information and high entity complexity in the process of entity recognition of grain harvesting machinery maintenance knowledge, a named entity recognition model XLNet—BiLSTM—AFF—CRF for grain harvesting machinery maintenance knowledge based on attention mechanism feature fusion is proposed. Firstly, the generalized autoregressive XLNet pre‑trained model based on Transformer—XL is used as the embedding layer to extract word vectors. Secondly, the Bidirectional Long Short‑Term Memory (BiLSTM) is used to obtain contextual semantic features. Thirdly, the attentional feature fusion (AFF) is used to combine the output of the XLNet layer with the output of the BiLSTM layer to enrich the semantic information of the sequence. Finally, the conditional random field (CRF) model is input to learn the annotation constraint rules to generate the global optimal sequence. Experiments are carried out on the created maintenance corpus. The experimental results show that the accuracy rate, recall rate and F1 value of the proposed model in this paper are 98.4%, 97.6% and 97.9%, respectively, which are higher than those of the comparison model, verifying the effectiveness of the model in this essay.

Key words: grain harvesting machinery, maintenance, named entity recognition, attention mechanism, generalized autoregressive pretraining for language understanding (XLNet)