GNNs-For-NLP

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GNNs-for-NLP is an open-source research and education tool that provides code examples and tutorials for Graph Neural Networks in Natural Language Processing. It includes implementations compatible with PyTorch and TensorFlow.

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Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is GNNs-for-NLP?

GNNs-for-NLP is a comprehensive resource offering code examples and tutorial materials for applying Graph Neural Networks (GNNs) to Natural Language Processing (NLP) tasks. Originating from presentations at EMNLP 2019 and CODS-COMAD 2020, this GitHub repository provides practical implementations using PyTorch 1.x and TensorFlow 1.x, compatible with Python 3.x. It features simplified GCN model implementations, extensions for relation extraction and word embeddings, and additional resources like theoretical write-ups and recent GNN papers. This tool is ideal for researchers and students looking to understand and implement graph-based deep learning methods in NLP.

Best used for

Ideal for professors and teachers who need to provide practical examples and educational materials for Graph Neural Networks in Natural Language Processing. Especially valuable for those teaching advanced NLP courses or conducting research in graph-based deep learning, offering ready-to-use code and theoretical resources.

Common actions

learn graph neural networks

implement NLP models

explore deep learning

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Capabilities

Key features

PyTorch GCN implementation
TensorFlow GCN implementation
Relation Extraction extensions
Word Embedding GCNs
Theoretical GCN writeup
Recent GNN papers

Target Audience

professorteacher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What programming languages and frameworks are supported by GNNs-for-NLP?

GNNs-for-NLP is compatible with Python 3.x and provides implementations for both PyTorch 1.x and TensorFlow 1.x. This allows users to work with their preferred deep learning framework for GNN applications in NLP.

Does GNNs-for-NLP offer examples for specific NLP tasks?

Yes, the repository includes extensions of the GCN implementation for various NLP problems. These include relation extraction (RESIDE), GCNs for word embeddings (WordGCN), and document time-stamping (NeuralDater), providing practical application contexts.

Are there theoretical resources available alongside the code examples?

Absolutely. GNNs-for-NLP provides a short write-up on the theory behind Graph Convolutional Networks, specifically Chapter 2, and also links to a collection of recent GNN papers. This helps users understand the underlying concepts.

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