结合监督学习与强化学习处理带部分标签的多标签分类任务
Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels
AI总结：
针对仅部分正类有标注的多标签正样本-未标记学习问题，提出结合强化学习探索能力与监督学习利用能力的MLPAC框架，在多类任务上验证了其泛化性与有效性。
摘要：
传统监督学习高度依赖人工标注数据集，在数据需求大的神经网络方法中尤为如此。然而，各类任务，尤其是文档级关系抽取这类多标签任务，因需要特定领域知识且类别集合庞大，全人工标注存在难度。为此，我们研究仅标注了部分正类的多标签正样本-未标记学习（MLPUL）问题。我们提出部分标注分类混合学习器（MLPAC），这是一种结合强化学习的探索能力与监督学习的利用能力的基于RL的框架。在文档级关系抽取、多标签图像分类、二分类PU学习等多种任务上的实验结果，证明了该框架的泛化性与有效性。
英文摘要：
Traditional supervised learning heavily relies on human-annotated datasets, especially in data-hungry neural approaches. However, various tasks, especially multi-label tasks like document-level relation extraction, pose challenges in fully manual annotation due to the specific domain knowledge and large class sets. Therefore, we address the multi-label positive-unlabelled learning (MLPUL) problem, where only a subset of positive classes is annotated. We propose Mixture Learner for Partially Annotated Classification (MLPAC), an RL-based framework combining the exploration ability of reinforcement learning and the exploitation ability of supervised learning. Experimental results across various tasks, including document-level relation extraction, multi-label image classification, and binary PU learning, demonstrate the generalization and effectiveness of our framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
