Awesome Relation Extraction
📖 A curated list of awesome resources dedicated to Relation Extraction, one of the most important tasks in Natural Language Processing (NLP).
This page lists names, links and short descriptions. The original list on GitHub is the source and belongs to its authors.
Research Trends and Surveys
Named Entity Recognition and Relation Extraction:State-of-the-Art
(Nasar et al., 2021)
A Survey on Relation Extraction
(Bach and Badaskar, 2017)
Relation Extraction: A Survey
(Pawar et al., 2017)
A Review on Entity Relation Extraction
(Zhang et al., 2017)
Review of Relation Extraction Methods: What is New Out There?
(Konstantinova et al., 2014)
Videos and Lectures
Stanford University: CS124
, Dan Jurafsky; (Video) Week 5: Relation Extraction and Question
Washington University: CSE517
, Luke Zettlemoyer; (Slide) Relation Extraction 1; (Slide) Relation Extraction 2
New York University: CSCI-GA.2590
, Ralph Grishman; (Slide) Relation Extraction: Rule-based Approaches
Michigan University: Coursera
, Dragomir R. Radev; (Video) Lecture 48: Relation Extraction
Virginia University: CS6501-NLP
, Kai-Wei Chang; (Slide) Lecture 24: Relation Extraction
Systems
DeepDive
DeepDive is a system to extract value from dark data. Like dark matter, dark data is the great mass of data buried in text, tables, figures, and images, which lacks structure and so is essentially unprocessable by existing software. License: Creative Commons Attribution 4.0.
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