Extracting multiple relations from text sentences is still a challenge for current Open Relation Extraction (Open RE) tasks. In this paper, we develop several Open RE models based on the bidirectional LSTM-CRF (BiLSTM-CRF) neural network and different contextualized word embedding methods. We also propose a new tagging scheme to solve overlapping problems and enhance models' performance. From the evaluation results and comparisons between models, we select the best combination of tagging scheme, word embedder, and BiLSTM-CRF network to achieve an Open RE model with a remarkable extracting ability on multiple-relation sentences.

翻译：从文本句子中抽取多重关系仍是当前开放关系抽取任务面临的挑战。本文基于双向LSTM-CRF神经网络与不同的上下文词嵌入方法，构建了若干开放关系抽取模型。我们同时提出了一种新的标注方案以解决重叠关系问题并提升模型性能。通过评估结果与模型对比，我们筛选出最优的标注方案、词嵌入器与BiLSTM-CRF网络组合，构建出在多重关系句子上具有显著抽取能力的开放关系抽取模型。