论文标题
使用主动学习和弱监督学习实体名称的结构化表示
Learning Structured Representations of Entity Names using Active Learning and Weak Supervision
论文作者
论文摘要
实体名称的结构化表示对于许多与实体相关的任务（例如实体归一化和变体生成）都是有用的。学习没有上下文和外部知识的实体名称的隐性结构化表示特别具有挑战性。在本文中，我们提出了一个新颖的学习框架，该框架结合了积极的学习和薄弱的监督以解决这个问题。我们的实验评估表明，该框架可以从只有十几个示例中学习高质量模型。
Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation. Learning the implicit structured representations of entity names without context and external knowledge is particularly challenging. In this paper, we present a novel learning framework that combines active learning and weak supervision to solve this problem. Our experimental evaluation show that this framework enables the learning of high-quality models from merely a dozen or so labeled examples.
