A joint extraction method for fault text entity relationships in smart grid considering nested entities and complex semantics

Abstract

Entity relationship extraction is the core task of text mining and intelligent retrieval, which can automatically extract the semantic relationships between entities. For a problem of low accuracy of information extraction due to semantic complexity and nested entities in the text in the field of electric power communication network, this paper proposes an Orth-Biaff-CasRel method for joint extraction of entity relationships in faulty text. The method incorporates the RoFormer of rotational position as a coding layer in the encoding process, capturing the relative positional relationships between entities in the text; In addition, considers the entity content information and entity boundary information, designs the head entity extraction method with Orthogonalized Biaffine attention mechanism. Finally, combining stacked pointers and adding hidden layers in the joint extraction of tail entities and relationships to achieve accurate extraction of information from complex semantic fault texts. The proposed method is experimentally verified in real fault texts of power communication networks and achieves good results, significantly outperforming the existing methods in terms of accuracy, recall and Fl value, with an improvement of 4.41 %,1.43 % and 2.92 %, respectively.