RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation (CIKM'17)

Overview

RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation

This is the implementation of RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation.

Code

To run our code, please use the following commands:

g++ RATE.cpp -o RATE -std=c++11
./RATE [Training File] [Test File] [L, optional, default = 30] [T, optional, default = 1]

For example,

g++ RATE.cpp -o RATE -std=c++11
./RATE Dataset/train.txt Dataset/test.txt 40 1

The prediction results will be in ./result.txt (the first row is the classification result). Then you can run

python eval.py

to obtain evaluation metrics.

Dataset

We release the Europe dataset (Dataset/data.json), where each line is a json file with tweet text and metadata. Due to privacy issues, we have anonymized the whole dataset by representing each word/feature as an integer. An example is shown below.

{ 
   "label":0,
   "language":"3",
   "timezone":"5",
   "offset":"7",
   "userlang":"5",
   "latitude":"36.8901",
   "longitude":"30.6809",
   "text":"3332 2608 29"
}

Given the json file, one can run

cd Dataset/
python preprocess.py

to get training and testing data (Dataset/train.txt and Dataset/test.txt).

Result

Method Micro-F1 (Acc) Macro-F1 Mean Distance Error (km) [email protected]
RATE 0.8905 0.5230 365.16 0.4315

Citation

@inproceedings{zhang2017rate,
  title={RATE: Overcoming Noise and Sparsity of Textual Features in Real-Time Location Estimation},
  author={Zhang, Yu and Wei, Wei and Huang, Binxuan and Carley, Kathleen M and Zhang, Yan},
  booktitle={Proceedings of the 2017 ACM on Conference on Information and Knowledge Management},
  pages={2423--2426},
  year={2017},
  organization={ACM}
}
Owner
Yu Zhang
CS Ph.D. student at UIUC; Data Mining
Yu Zhang
a Lightweight library for sequential learning agents, including reinforcement learning

SaLinA: SaLinA - A Flexible and Simple Library for Learning Sequential Agents (including Reinforcement Learning) TL;DR salina is a lightweight library

Facebook Research 405 Dec 17, 2022
Use deep learning, genetic programming and other methods to predict stock and market movements

StockPredictions Use classic tricks, neural networks, deep learning, genetic programming and other methods to predict stock and market movements. Both

Linda MacPhee-Cobb 386 Jan 03, 2023
这是一个yolox-keras的源码,可以用于训练自己的模型。

YOLOX:You Only Look Once目标检测模型在Keras当中的实现 目录 性能情况 Performance 实现的内容 Achievement 所需环境 Environment 小技巧的设置 TricksSet 文件下载 Download 训练步骤 How2train 预测步骤 Ho

Bubbliiiing 64 Nov 10, 2022
AoT is a system for automatically generating off-target test harness by using build information.

AoT: Auto off-Target Automatically generating off-target test harness by using build information. Brought to you by the Mobile Security Team at Samsun

Samsung 10 Oct 19, 2022
Codes for "Template-free Prompt Tuning for Few-shot NER".

EntLM The source codes for EntLM. Dependencies: Cuda 10.1, python 3.6.5 To install the required packages by following commands: $ pip3 install -r requ

77 Dec 27, 2022
TensorFlow2 Classification Model Zoo playing with TensorFlow2 on the CIFAR-10 dataset.

Training CIFAR-10 with TensorFlow2(TF2) TensorFlow2 Classification Model Zoo. I'm playing with TensorFlow2 on the CIFAR-10 dataset. Architectures LeNe

Chia-Hung Yuan 16 Sep 27, 2022
A hybrid framework (neural mass model + ML) for SC-to-FC prediction

The current workflow simulates brain functional connectivity (FC) from structural connectivity (SC) with a neural mass model. Gradient descent is applied to optimize the parameters in the neural mass

Yilin Liu 1 Jan 26, 2022
Aydin is a user-friendly, feature-rich, and fast image denoising tool

Aydin is a user-friendly, feature-rich, and fast image denoising tool that provides a number of self-supervised, auto-tuned, and unsupervised image denoising algorithms.

Royer Lab 99 Dec 14, 2022
Omniscient Video Super-Resolution

Omniscient Video Super-Resolution This is the official code of OVSR (Omniscient Video Super-Resolution, ICCV 2021). This work is based on PFNL. Datase

36 Oct 27, 2022
FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation.

FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation [Project] [Paper] [arXiv] [Home] Official implementation of FastFCN:

Wu Huikai 815 Dec 29, 2022
A general-purpose programming language, focused on simplicity, safety and stability.

The Rivet programming language A general-purpose programming language, focused on simplicity, safety and stability. Rivet's goal is to be a very power

The Rivet programming language 17 Dec 29, 2022
Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition

GCN_LogsigRNN This repository holds the codebase for the paper: Logsig-RNN: a novel network for robust and efficient skeleton-based action recognition

7 Oct 14, 2022
This is the official implementation of VaxNeRF (Voxel-Accelearated NeRF).

VaxNeRF Paper | Google Colab This is the official implementation of VaxNeRF (Voxel-Accelearated NeRF). This codebase is implemented using JAX, buildin

naruya 132 Nov 21, 2022
Official PyTorch implementation of StyleGAN3

Modified StyleGAN3 Repo Changes Made tied to python 3.7 syntax .jpgs instead of .pngs for training sample seeds to recreate the 1024 training grid wit

Derrick Schultz (he/him) 83 Dec 15, 2022
buildseg is a building extraction plugin of QGIS based on PaddlePaddle.

buildseg buildseg is a building extraction plugin of QGIS based on PaddlePaddle. TODO Extract building on 512x512 remote sensing images. Extract build

Yizhou Chen 11 Sep 26, 2022
Official implementation of the NeurIPS'21 paper 'Conditional Generation Using Polynomial Expansions'.

Conditional Generation Using Polynomial Expansions Official implementation of the conditional image generation experiments as described on the NeurIPS

Grigoris 4 Aug 07, 2022
FastyAPI is a Stack boilerplate optimised for heavy loads.

FastyAPI A FastAPI based Stack boilerplate for heavy loads. Explore the docs » View Demo · Report Bug · Request Feature Table of Contents About The Pr

Ali Chaayb 47 Dec 27, 2022
天勤量化开发包, 期货量化, 实时行情/历史数据/实盘交易

TqSdk 天勤量化交易策略程序开发包 TqSdk 是一个由信易科技发起并贡献主要代码的开源 python 库. 依托快期多年积累成熟的交易及行情服务器体系, TqSdk 支持用户使用极少的代码量构建各种类型的量化交易策略程序, 并提供包含期货、期权、股票的 历史数据-实时数据-开发调试-策略回测-

信易科技 2.8k Dec 30, 2022
Custom implementation of Corrleation Module

Pytorch Correlation module this is a custom C++/Cuda implementation of Correlation module, used e.g. in FlowNetC This tutorial was used as a basis for

Clément Pinard 361 Dec 12, 2022