计算机科学
Boosting(机器学习)
训练集
卷积神经网络
人工智能
机器学习
随机森林
掉期(金融)
模式识别(心理学)
数据挖掘
财务
经济
作者
Jason Zhanshun Wei,Kai Zou
出处
期刊:Empirical Methods in Natural Language Processing
日期:2019-03-05
被引量:645
摘要
We present EDA: easy data augmentation techniques for boosting performance on text classification tasks. EDA consists of four simple but powerful operations: synonym replacement, random insertion, random swap, and random deletion. On five text classification tasks, we show that EDA improves performance for both convolutional and recurrent neural networks. EDA demonstrates particularly strong results for smaller datasets; on average, across five datasets, training with EDA while using only 50% of the available training set achieved the same accuracy as normal training with all available data. We also performed extensive ablation studies and suggest parameters for practical use.
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