DERE: A Task and Domain-Independent Slot Filling Framework for Declarative Relation Extraction (D18-2)
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| Challenge: | Comparability of models across tasks is lacking in most machine learning systems for natural language processing. |
| Approach: | They propose a framework for declarative specification and compilation of template-based information extraction that uses a generic specification language for the task and for data annotations in terms of spans and frames. |
| Outcome: | The proposed framework enables representation of a large variety of natural language processing tasks. |
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Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling (2022.findings-emnlp)
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| Challenge: | Document-level relation extraction (DocRE) aims to identify semantic labels among entities within a document. |
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| Challenge: | Existing models for natural language processing (NLP) do not address common tasks. |
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Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction (N19-1)
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