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

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Published:22 September 2021Publication History
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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 texts. 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 (GAT) is an improvement of graph convolutional neural networks (GCN). Compared with GCN, the advantage of GAT is that the attention mechanism is proposed to weight the sum of the characteristics of adjacent vertices. 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.

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      • Published in

        cover image ACM Transactions on Asian and Low-Resource Language Information Processing
        ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 20, Issue 5
        September 2021
        320 pages
        ISSN:2375-4699
        EISSN:2375-4702
        DOI:10.1145/3467024
        Issue’s Table of Contents

        Copyright © 2021 Association for Computing Machinery.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Accepted: 1 November 2021
        • Published: 22 September 2021
        • Revised: 1 October 2020
        • Received: 1 September 2020
        Published in tallip Volume 20, Issue 5

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