A Survey of Deep Learning Methods for Relation Extraction

Shantanu Kumar

A Survey of Deep Learning Methods for Relation Extraction

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arxiv 1705.03645 v1 pith:AF7KQPBH submitted 2017-05-10 cs.CL

Shantanu Kumar This is my paper · ORCID

classification cs.CL

keywords extractiondeeplearningmodelsrelationaheadbeencompare

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Relation Extraction is an important sub-task of Information Extraction which has the potential of employing deep learning (DL) models with the creation of large datasets using distant supervision. In this review, we compare the contributions and pitfalls of the various DL models that have been used for the task, to help guide the path ahead.

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Extracting Structured Requirements from Unstructured Building Technical Specifications for Building Information Modeling
cs.CL 2025-08 unverdicted novelty 4.0 of 10

A study showing that CamemBERT and Fr_core_news_lg achieve over 90% F1 for named entity recognition and Random Forest achieves over 80% F1 for relation extraction on French building technical specifications.

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