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

Code for models used in Bashiri et al., "A Flow-based latent state generative model of neural population responses to natural images".

Deep Watershed Transform for Instance Segmentation

Recovering Brain Structure Network Using Functional Connectivity

Hysterese plugin with two temperature offset areas

Deformable DETR is an efficient and fast-converging end-to-end object detector.

Lightweight Cuda Renderer with Python Wrapper.

A robust pointcloud registration pipeline based on correlation.

Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?

Reproducing-BowNet: Learning Representations by Predicting Bags of Visual Words

A tensorflow implementation of Fully Convolutional Networks For Semantic Segmentation

Demonstrates how to divide a DL model into multiple IR model files (division) and introduce a simplest way to implement a custom layer works with OpenVINO IR models.

The project covers common metrics for super-resolution performance evaluation.

AI-UPV at IberLEF-2021 EXIST task: Sexism Prediction in Spanish and English Tweets Using Monolingual and Multilingual BERT and Ensemble Models

Demonstrates iterative FGSM on Apple's NeuralHash model.

A library for building and serving multi-node distributed faiss indices.

Optimal Camera Position for a Practical Application of Gaze Estimation on Edge Devices,

Official PyTorch code for CVPR 2020 paper "Deep Active Learning for Biased Datasets via Fisher Kernel Self-Supervision"

Matplotlib Image labeller for classifying images

Computer vision - fun segmentation experience using classic and deep tools :)

Biomarker identification for COVID-19 Severity in BALF cells Single-cell RNA-seq data