Dependency Parsing-Based Syntactic Enhancement of Relation Extraction in Scientific Texts (2025.findings-emnlp)
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| Challenge: | a pipeline approach to extract entities and relations from scientific text is challenging due to long sentences with densely packed entities. |
| Approach: | They propose a syntactic filtering method that prunes unlikely entity pairs before relation prediction. |
| Outcome: | The proposed method improves Rel+ F1 scores on SciERC, SciER, and ACE05 datasets. |
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| Challenge: | Joint relation extraction models face high computational complexity, complex network architectures, difficult parameter tuning and limited interpretability. |
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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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| Challenge: | Existing methods for medical relation extraction use dependency syntax as a source of features. |
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| Challenge: | Existing frameworks for relation extraction use distant supervision instead of annotated data. |
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| Challenge: | Existing studies require modifications to existing baseline architectures to leverage syntactic information. |
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GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction (2021.findings-acl)
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| Challenge: | a funder name refers to an agency, organization, or program providing financial support for the research. |
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| Challenge: | Existing RE surveys focus on modeling techniques, but there are few that are based on real-world scenarios. |
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| Challenge: | Scientific information extraction (SciIE) is critical for converting unstructured knowledge from scholarly articles into structured data. |
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Entity-centered Cross-document Relation Extraction (2022.emnlp-main)
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Fengqi Wang, Fei Li, Hao Fei, Jingye Li, Shengqiong Wu, Fangfang Su, Wenxuan Shi, Donghong Ji, Bo Cai
| Challenge: | Existing methods for relation extraction only use text snippets surrounding target entities in multiple documents. |
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