A Method for Extracting Power Entity Relationships Based on Hybrid Neural Networks

Abstract

In response to the challenges of entity relationship extraction in unstructured text, the author proposes a power entity relationship extraction method based on a hybrid neural network. This method aims to overcome the limitations of existing models in accurately representing contextual environment information, thereby improving the accuracy of the extraction model to meet practical application needs. Firstly, a Bidirectional Gated Recurrent Unit (BiGRU) was designed to better capture contextual information in text sequences. This helps the model to better understand the relationships between entities. Secondly, an attention mechanism was adopted to enable the model to automatically focus on sequence features that have a significant impact on relationships. This helps the model to extract entity relationships more accurately in complex text environments. Finally, a segmented convolutional neural network (PCNN) was introduced to further improve the accuracy of relationship extraction by learning the environmental feature information in the adjusted sequence. This enables the model to better understand the contextual relationships between entities. On the publicly available English dataset SemEval2010Task8, this method achieved satisfactory results, achieving an F1 value of 85.62%. These experiments have confirmed the effectiveness of our method, providing new ideas and support for automatic extraction of entity relationships, and are expected to play an important role in the field of information extraction.