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- W3159819457 abstract "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." @default.
- W3159819457 created "2021-05-10" @default.
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- W3159819457 date "2021-04-25" @default.
- W3159819457 modified "2023-10-16" @default.
- W3159819457 title "Explore BiLSTM-CRF-Based Models for Open Relation Extraction" @default.
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- W3159819457 doi "https://doi.org/10.48550/arxiv.2104.12333" @default.
- W3159819457 hasPublicationYear "2021" @default.
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