MORE: A Metric Learning Based Framework for Open-domain Relation Extraction
This work addresses inefficiencies in open-domain relation extraction for NLP applications, offering a novel approach that improves clustering and extraction tasks.
The paper tackles the problem of open relation extraction by proposing MORE, a metric learning-based framework that learns semantic relational representations directly from labeled data, achieving state-of-the-art performance on two real-world datasets.
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.