Probabilistic Graph Attention for Relation Extraction for Domain of Geography

Abstract

In view of the lack of labeled corpus and the difficulty of extracting multiple relations in the extraction of entity relation in the geographic domain, a method based on probabilistic graph is proposed for extracting the entity relation in the geographic domain. This method uses the semantic information in the knowledge base to enhance the representation of the geographic domain corpus to alleviate the problem of insufficient labeled corpus. It uses character-word hybrid vectors that can be effectively integrated into the semantic information as the feature vectors. The vectors are transmitted to Bi-LSTM and self-attention for global deep feature extraction. Finally drawing on the idea of probabilistic graph, the "semi pointer-semi annotation" method is utilized to extract the head entities, traverse the head entity, and then uses the same method to extract tail entities and relations. By comparing the experimental results on the geographic domain corpus and ACE05 corpus with other advanced methods, the probabilistic graph-based extraction method effectively improves the geographic domain entity relation extraction effect.