L3E-HD: A Framework Enabling Efficient Ensemble in High-Dimensional Space for Language Tasks
overview
This repository provides a implementation of the framework for language tasks.
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Code
hdc.py- implementation of HDC framework, using ngram for encoding and hamming distance for similarity calculation.
adaboost.py- implementation of adaboost framework, using HdC framework as the classifier.
main.py- provides different datasets and paraameters for user to choose.
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dataset
language- Language classification task.
SST-2- The Stanford Sentiment Treebank from GLUE, for sentiment classification task.
ag_news_csv- News articles classification task.
spam.csv- Text spam classification task.
Youtube-all- Youtube comment spam classification task.
Run model
Example
python main.py --task-id 5 --classifiers 4 --boost-lr 1.0 --dim 2000 --ngram 4 --retrain-rounds 0 --hdc-lr 0.0005Parameters
--task-id- from 1 to 5, indicating different tasks
--classifiers- the number of classifiers for the boost framework
--boost-lr- learning rate of boost framework
--dim- dimension of HDC framework
--ngram- value of n for ngram encoding method in HDC framework
--retrain-rounds- iterations of retraining in HDC framework
--hdc-lr- learning rate of HDC framework
Citation
We now have a paper, titled "L3E-HD: A Framework Enabling Efficient Ensemble in High-Dimensional Space for Language Tasks", which is published in SIGIR-2022.
@inproceedings{liu2021L3EHD,
title={L3E-HD: A Framework Enabling Efficient Ensemble in High-Dimensional Space for Language Tasks},
author={Liu, Fangxin and Li, Haomin and Jiang, Li},
booktitle={Proceedings of the International ACM Sigir Conference on Research and Development in Information Retrieval (SIGIR)},
year={2022}
}