关键词: 风电能源装备, 嵌套命名实体识别, 差分语义编码, 多头双仿射编码, 跨度, 卷积注意力机制

Abstract: Abstract: The high nesting of entities and the characteristics of long texts in the field of wind power equipment were addressed by proposing a nested named entity recognition model based on differential boundary enhancement (DBE-NER). Firstly, a semantic encoder module was used to obtain feature representations that fused the entity's head and tail words, entity types, and relative distances, enhancing the model's ability to capture nested semantic features. Secondly, an efficient differential semantic encoding module was designed to resolve the ambiguity of nested entity boundaries. Furthermore, a grouped dilated attention network was utilized to improve the model's effectiveness in recognizing long-text entities, nested entities, and nested boundaries. Finally, the feature score matrix was input into a span decoder to obtain the positions and categories of entities. Experimental results indicated that the F1 score of DBE-NER improved by 0.92% and 1.07% compared to the DIFINET (Boundary-Aware Semantic Differentiation and Filtration Network) and CNN-NER (Convolutional Neural Network for Nested Named Entity Recognition) models on a manually annotated dataset of fault data from a large wind power energy enterprise (WPEF). The F1 score also increased on various public datasets.

Key words: wind energy equipment, nested name entity recognition, differential semantic encoder, muti-head biaffine encoder, span, convolutional block attention module