Spatial Relationship Extraction of Geographic Entities Based on BERT Model

Abstract

Geographic entity relationship extraction from text is an important way to acquire geographic knowledge. Entity relations in Chinese text are difficult to discover because of implicit representations of relations between entities in Chinese text. Therefore, using existing pattern matching and machine learning methods to extract entity relations from Chinese text often has problems such as insufficient artificial features, poor generality, inability to resolve word polysemy, and difficulty in making full use of contextual information. However, deep learning methods can better solve the above problems. This paper takes the spatial relation between geographic entities as the main research object to build a geographic entity spatial relation corpus, and propose a geographic entity relationship extraction method that combines BERT and attention mechanism. The method is trained and tested on the corpus. The results show that the F1 value of the BERT-BiGRU-Attention model proposed in this paper reaches 85.2% on the test set, which is a great improvement in relation extraction ability compared with other baseline models. The geographic entity relationship extraction method based on the BERT model can effectively learn the context information in Chinese text sentences, improve the accuracy of relationship extraction, and provide support for geographic knowledge graph construction and geographic entity information retrieval.