用于Twitter地理位置预测和哈希的端到端网络
End-to-end Network for Twitter Geolocation Prediction and Hashing
摘要：
我们提出了一个端到端的神经网络来预测推文的地理位置。该网络将许多原始Twitter元数据作为输入，例如推文消息和相关的用户帐户信息。我们的模型是独立于语言的，尽管最小的特征工程，它是可解释的，能够学习位置指示词和定时模式。与最先进的系统相比，我们的模型比它们高出2%-6%。此外，我们还提出了对模型的扩展，将网络学习的表示压缩为二进制代码。实验表明，它产生紧凑的代码相比，基准散列算法。该模型的实现被公开发布。
英文摘要：
We propose an end-to-end neural network to predict the geolocation of a tweet. The network takes as input a number of raw Twitter metadata such as the tweet message and associated user account information. Our model is language independent, and despite minimal feature engineering, it is interpretable and capable of learning location indicative words and timing patterns. Compared to state-of-the-art systems, our model outperforms them by 2%-6%. Additionally, we propose extensions to the model to compress representation learnt by the network into binary codes. Experiments show that it produces compact codes compared to benchmark hashing algorithms. An implementation of the model is released publicly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
