| Challenge: | Existing semi-supervised bootstrapping methods for relationship extraction lack labeled data. |
| Approach: | They propose a semi-supervised bootstrapping method that protects against semantic drift . they expand entities and templates in parallel and in mutually constraining fashion in each iteration . |
| Outcome: | Experimental results show that BREX improves on state-of-the-art methods for four relationships. |
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| Challenge: | Existing methods for entity set expansion define the expansion boundary using seed-based distance metrics, which are hard to adjust due to the extremely sparse supervision. |
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| Challenge: | Existing methods for supervised relation extraction still require a large quantity of training data. |
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| Challenge: | Existing work on end-to-end relation extraction models combine two tasks: named entity recognition and relation extraction. |
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Extracting Entities and Relations with Joint Minimum Risk Training (D18-1)
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Span-Level Model for Relation Extraction (P19-1)
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ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction (2021.eacl-main)
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An Empirical Study of Pipeline vs. Joint approaches to Entity and Relation Extraction (2022.aacl-short)
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| Challenge: | Entity and Relation Extraction tasks are often compared to pipeline approaches . a recent study shows that joint approaches can produce comparable results . |
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