论文标题
通过增强学习，知识引导的开放属性价值提取
Knowledge-guided Open Attribute Value Extraction with Reinforcement Learning
论文作者
论文摘要
新兴实体的开放属性值提取是一项重要但具有挑战性的任务。许多以前的作品都将问题作为一个\ textit {Question-wever}（QA）任务。尽管Web语料库的文章收集提供了有关新兴实体的最新信息，但检索到的文本可能是嘈杂的，无关紧要的，因此导致了不准确的答案。有效地滤除嘈杂的文章以及不良答案是提高提取准确性的关键。知识图（KG）包含有关实体的丰富，组织良好的信息，它提供了一个很好的资源来应对挑战。在这项工作中，我们为开放属性提取的知识引导的增强学习（RL）框架提出。在KG中相关知识的情况下，我们训练了一个深Q网络，以依次比较提取的答案以提高提取精度。所提出的框架适用于不同的信息提取系统。我们的实验结果表明，我们的方法的表现优于16.5-27.8 \％。
Open attribute value extraction for emerging entities is an important but challenging task. A lot of previous works formulate the problem as a \textit{question-answering} (QA) task. While the collections of articles from web corpus provide updated information about the emerging entities, the retrieved texts can be noisy, irrelevant, thus leading to inaccurate answers. Effectively filtering out noisy articles as well as bad answers is the key to improving extraction accuracy. Knowledge graph (KG), which contains rich, well organized information about entities, provides a good resource to address the challenge. In this work, we propose a knowledge-guided reinforcement learning (RL) framework for open attribute value extraction. Informed by relevant knowledge in KG, we trained a deep Q-network to sequentially compare extracted answers to improve extraction accuracy. The proposed framework is applicable to different information extraction system. Our experimental results show that our method outperforms the baselines by 16.5 - 27.8\%.
