JohnGiorgi/seq2rel
The corresponding code for our paper: A sequence-to-sequence approach for document-level relation extraction.
This project helps scientific researchers and data analysts automatically identify and extract relationships between key entities, such as genes and diseases, from scientific literature or other textual data. You provide a document or a collection of texts, and it outputs structured information detailing which entities are related and the type of relationship they share. It's designed for anyone who needs to quickly pinpoint crucial connections within large volumes of text.
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Use this if you need to systematically extract specific types of relationships (like gene-disease associations) from unstructured text, especially in scientific or biomedical fields.
Not ideal if your goal is general-purpose information extraction without predefined relationship types, or if you only need to identify entities without their connections.
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Jupyter Notebook
License
Apache-2.0
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Last pushed
May 27, 2024
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