geoGAT: Graph Model Based on Attention Mechanism for Geographic Text Classification

TL;DR

geoGAT introduces an attention-based graph neural network for classifying geographic texts in Chinese, achieving high accuracy and addressing a research gap in geographic text classification.

Contribution

This work develops a novel geoGAT model utilizing graph attention networks for geographic text classification in Chinese, with a new dataset and state-of-the-art performance.

Findings

Macro-F Score of 95% on Chinese geographic dataset

Effective extraction of geographic entities from network texts

Addresses gap in geographic text classification research

Abstract

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.