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

Last update: Feb 10, 2022

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}
}

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

Related tags

Overview

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

Code

Dataset

Result

Citation

Owner

Yu Zhang

[WACV 2022] Contextual Gradient Scaling for Few-Shot Learning

YOLOX-RMPOLY

Data Augmentation Using Keras and Python

Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

Pytorch implementation of Generative Models as Distributions of Functions 🌿

This is the code repository implementing the paper "TreePartNet: Neural Decomposition of Point Clouds for 3D Tree Reconstruction".

Code for the ICASSP-2021 paper: Continuous Speech Separation with Conformer.

Compares various time-series feature sets on computational performance, within-set structure, and between-set relationships.

Simple tool to combine(merge) onnx models. Simple Network Combine Tool for ONNX.

IEEE Winter Conference on Applications of Computer Vision 2022 Accepted

ElegantRL is featured with lightweight, efficient and stable, for researchers and practitioners.

Deep Inside Convolutional Networks - This is a caffe implementation to visualize the learnt model

Object detection evaluation metrics using Python.

Implements MLP-Mixer: An all-MLP Architecture for Vision.

A robust camera and Lidar fusion based velocity estimator to undistort the pointcloud.

The dataset and source code for our paper: "Did You Ask a Good Question? A Cross-Domain Question IntentionClassification Benchmark for Text-to-SQL"

Code repository for the paper "Tracking People with 3D Representations"

TumorInsight is a Brain Tumor Detection and Classification model built using RESNET50 architecture.

It's final year project of Diploma Engineering. This project is based on Computer Vision.

An AFL implementation with UnTracer (our coverage-guided tracer)