yul091/DENRL
Distantly-Supervised Joint Entity and Relation Extraction with Noise-Robust Learning
This tool helps AI/ML researchers and data scientists improve the accuracy of information extraction from text when dealing with noisy, automatically labeled data. You provide text documents with distantly supervised labels (often generated by rules or weak heuristics), and it outputs a more reliable model for identifying entities and their relationships. It's ideal for those building robust natural language processing systems.
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Use this if you need to extract specific pieces of information (like names, places, or events) and the connections between them from large text datasets where the initial labeling might be inaccurate or incomplete.
Not ideal if your data is already perfectly hand-labeled or if you are looking for a pre-trained, plug-and-play solution without custom model training.
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Python
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Last pushed
May 25, 2024
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