malllabiisc/RESIDE
EMNLP 2018: RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information
This project helps knowledge engineers and data analysts automatically identify specific relationships between entities mentioned in large text corpora, like news articles or academic papers. It takes raw text and existing knowledge graph information as input, then outputs extracted relationships. The primary users are researchers or practitioners working with information extraction from unstructured text to build or enrich knowledge bases.
250 stars. No commits in the last 6 months.
Use this if you need to extract specific types of relationships (e.g., 'located in', 'employs') between entities from a large collection of sentences, especially when you can leverage existing side information like entity types or relation aliases to improve accuracy.
Not ideal if your goal is general text summarization, sentiment analysis, or if you do not have access to any form of 'side information' to aid the relation extraction process.
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Language
CSS
License
Apache-2.0
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Last pushed
Mar 24, 2023
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