A Bag-of-concepts Model Improves Relation Extraction in a Narrow Knowledge Domain with Limited Data (N19-3)
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| Challenge: | Existing methods for relation extraction on small data sets are time-consuming and expensive. |
| Approach: | They propose an automatic relation extraction task with limited annotated data and a narrow knowledge domain. |
| Outcome: | The proposed method outperforms methods of higher complexity on a small clinical corpus. |
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| Challenge: | Document-level Relation Extraction (DocRE) is a task that aims to extract relations from a long context. |
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| Challenge: | Low-resource relation extraction aims to identify semantic relationships using scarce labeled data. |
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| Challenge: | Existing frameworks for relation extraction use distant supervision instead of annotated data. |
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| Challenge: | Recent approaches to distantly supervised relation extraction (DS-RE) encode each sentence in an entity-pair bag separately. |
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| Challenge: | Existing FSRE methods fail to classify relations based on information of sentences and entity pairs due to limited samples and lack of knowledge. |
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| Challenge: | Existing Relation extraction models require extensive annotated training data, which is costly and labor-intensive to collect. |
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More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020.aacl-main)
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Xu Han, Tianyu Gao, Yankai Lin, Hao Peng, Yaoliang Yang, Chaojun Xiao, Zhiyuan Liu, Peng Li, Jie Zhou, Maosong Sun
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| Challenge: | Existing approaches to handle wrong labeling and long-tail relations are labor-intensive and scarce training data. |
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| Challenge: | Using a pre-trained BERT-Base model, we learn domain-specific language representations using biomedical text. |
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