  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.
