geoGAT: Graph Model Based on Attention Mechanism for Geographic Text Classification
This work addresses a gap in geographic information processing for Chinese text, with potential applications in transportation and urban planning, but it is incremental as it adapts existing methods to a new domain.
The authors tackled geographic text classification in Chinese by implementing a graph attention network (geoGAT) to extract geographic entities from network text, achieving a Macro-F Score of 95% on a new Chinese dataset.
In the area of geographic information processing. There are few researches on geographic text classification. However, the application of this task in Chinese is relatively rare. In our work, we intend to implement a method to extract text containing geographical entities from a large number of network text. The geographic information in these texts is of great practical significance to transportation, urban and rural planning, disaster relief and other fields. We use the method of graph convolutional neural network with attention mechanism to achieve this function. Graph attention networks is an improvement of graph convolutional neural networks. Compared with GCN, the advantage of GAT is that the attention mechanism is proposed to weight the sum of the characteristics of adjacent nodes. In addition, We construct a Chinese dataset containing geographical classification from multiple datasets of Chinese text classification. The Macro-F Score of the geoGAT we used reached 95\% on the new Chinese dataset.