INK-USC/shifted-label-distribution
Source code for paper "Looking Beyond Label Noise: Shifted Label Distribution Matters in Distantly Supervised Relation Extraction" (EMNLP 2019)
This project helps natural language processing researchers and practitioners extract relationships between entities in text, even when dealing with noisy, large datasets. It takes a collection of sentences with identified entities and outputs improved models for relation extraction. This is for professionals working on automated information extraction, knowledge graph creation, or advanced text understanding systems.
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Use this if you are developing relation extraction models using distant supervision and suspect that varying data distributions between training and application datasets are hindering performance.
Not ideal if you are looking for an out-of-the-box, end-user application for relation extraction without needing to train or adapt models.
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Language
C++
License
Apache-2.0
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Last pushed
Oct 29, 2019
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