Semi-Supervised Learning with Auxiliary Evaluation Component for Large Scale e-Commerce Text Classification
Mingkuan Liu, Musen Wen, Selçuk Köprü, Xianjing Liu, Alan Lu · 2018
The lack of high-quality labeled training data has been one of the critical challenges facing many industrial machine learning tasks.To tackle this challenge, in this paper, we propose a semi-supervised learning method to utilize unlabeled data and user feedback signals to improve the performance of ML models.The method employs a primary model M ain and an auxiliary evaluation model Eval, where M ain and Eval models are trained iteratively by automatically generating labeled data from unlabeled data and/or users feedback signals.The proposed approach is applied to different text classification tasks.We report results on both the publicly available Yahoo! Answers dataset and our e-commerce product classification dataset.The experimental results show that the proposed method reduces the classification error rate by 4% and up to 15% across various experimental setups and datasets.A detailed comparison with other semi-supervised learning approaches is also presented later in the paper.The results from various text classification tasks demonstrate that our method outperforms those developed in previous related studies.