基于先验知识的文本分类
Robustly Leveraging Prior Knowledge in Text Classification
摘要：
先验知识已经被证明是非常有用的，以解决许多自然语言处理任务。已经提出了许多方法来形式化各种知识，然而，所提出的方法是否是强大的或敏感的知识提供给模型很少讨论。在本文中，我们提出了三个正则化项上的广义期望标准，并进行了广泛的实验，以证明所提出的方法的鲁棒性。实验结果表明，我们提出的方法取得了显着的改善，比基线更强大。
英文摘要：
Prior knowledge has been shown very useful to address many natural language processing tasks. Many approaches have been proposed to formalise a variety of knowledge, however, whether the proposed approach is robust or sensitive to the knowledge supplied to the model has rarely been discussed. In this paper, we propose three regularization terms on top of generalized expectation criteria, and conduct extensive experiments to justify the robustness of the proposed methods. Experimental results demonstrate that our proposed methods obtain remarkable improvements and are much more robust than baselines.
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。
