论文标题
更多：开放域关系提取的基于公制的学习框架
MORE: A Metric Learning Based Framework for Open-domain Relation Extraction
论文作者
论文摘要
开放关系提取（OpenRE）是从开放域CORPORA提取关系方案的任务。大多数现有的OpenRE方法要么无法完全受益于高质量标签的Corpora，要么无法直接学习语义表示，从而影响下游聚类效率。为了解决这些问题，在这项工作中，我们提出了一个新颖的学习框架，名为More（基于公制的开放关系提取）。该框架利用深度度量学习从标记的数据中获取丰富的监督信号，并驱动神经模型直接学习语义关系表示。实验导致两个现实世界数据集表明我们的方法的表现优于其他最先进的基线。我们的源代码可在GitHub上找到。
Open relation extraction (OpenRE) is the task of extracting relation schemes from open-domain corpora. Most existing OpenRE methods either do not fully benefit from high-quality labeled corpora or can not learn semantic representation directly, affecting downstream clustering efficiency. To address these problems, in this work, we propose a novel learning framework named MORE (Metric learning-based Open Relation Extraction). The framework utilizes deep metric learning to obtain rich supervision signals from labeled data and drive the neural model to learn semantic relational representation directly. Experiments result in two real-world datasets show that our method outperforms other state-of-the-art baselines. Our source code is available on Github.
