Awesome-Relation-Extraction

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awesome-relation-extraction is an Open Source resource for relation extraction in NLP. It provides a curated list of research papers, datasets, and frameworks for various approaches to relation extraction.

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Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is awesome-relation-extraction?

awesome-relation-extraction is a comprehensive, open-source curated list of resources dedicated to Relation Extraction, a crucial task in Natural Language Processing (NLP). This repository, inspired by other 'awesome' lists, compiles a wide array of research trends, surveys, and papers covering supervised, distant supervision, GNN-based, and language model approaches. It also features knowledge graph-based and few-shot learning methods. Additionally, the resource includes links to relevant datasets, videos, lectures, systems, and frameworks, making it an invaluable tool for researchers and practitioners looking to explore or advance their work in relation extraction.

Best used for

Ideal for professors, researchers, and students who need to explore the latest advancements in relation extraction, find relevant datasets for their projects, and discover new NLP frameworks. Especially valuable for those seeking to deepen their understanding of various relation extraction methodologies.

Common actions

explore research

find datasets

learn NLP techniques

discover frameworks

track research trends

workflowsopen-sourcecollaboration"AI Agents"automated workflowlow-code/no-codegithub copilotdeepfakeface swapping

Capabilities

Key features

Curated research papers
Relation extraction datasets
NLP frameworks
Video lectures
Research trends

Target Audience

professorresearcherstudent

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of relation extraction approaches are covered in this list?

The list covers a wide range of approaches including supervised, distant supervision, GNN-based models, and various language model representations (Encoder and Decoder from Transformer). It also includes knowledge graph-based and few-shot learning approaches, offering a comprehensive overview of the field.

Does this resource provide access to datasets for relation extraction?

Yes, awesome-relation-extraction includes a dedicated section for datasets. It lists popular datasets like SemEval-2010 Task 8, New York Times (NYT) Corpus, FewRel, TACRED, ACE05, and SemEval-2018 Task 7, often with links to papers and download information.

Are there any practical tools or frameworks mentioned for implementing relation extraction?

The resource features a 'Frameworks' section, highlighting tools like OpenNRE, an open-source and extensible toolkit for implementing neural models for relation extraction. It also lists 'Systems' such as DeepDive and Stanford Relation Extractor, providing practical options for users.

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