zhpmatrix/BERTem
论文实现(ACL2019)：《Matching the Blanks: Distributional Similarity for Relation Learning》
This project helps natural language processing researchers or practitioners extract relationships between entities from text. You input raw text containing mentions of entities, and it outputs classifications of the semantic relationships between those entities (e.g., 'person works at organization'). This is useful for anyone building systems that need to understand factual connections hidden in unstructured text data.
153 stars. No commits in the last 6 months.
Use this if you are a researcher or NLP engineer looking to implement or experiment with neural relation extraction models, particularly those based on BERT embeddings.
Not ideal if you need a complete, pre-trained, production-ready model for relation extraction without needing to supply your own datasets or pre-trained models for fine-tuning.
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Jupyter Notebook
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Apache-2.0
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Last pushed
Dec 08, 2022
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