geoGAT: Graph Model Based on Attention Mechanism for Geographic Text Classification
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 19 of its 38 references that are in the map. First posted on arXiv 13 Jan 2021.
Builds on
19 of 38 references.
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7 more
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Citing papers per year: 2021: 1.
1 paper.