Text_gcn

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text_gcn is an open-source Coding & Development tool that implements Graph Convolutional Networks for text classification. It allows researchers and developers to reproduce results from the AAAI 2019 paper.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is text_gcn?

text_gcn is an open-source implementation of Graph Convolutional Networks (GCNs) specifically designed for text classification tasks. This tool provides the necessary code to reproduce the results presented in the paper "Graph Convolutional Networks for Text Classification" from the AAAI 2019 conference. It requires Python 2.7 or 3.6 and Tensorflow >= 1.4.0, making it accessible for those familiar with these environments. The repository includes scripts for data preparation, graph building, and model training, along with examples for various datasets like 20ng, R8, R52, ohsumed, and mr. An inductive version, fast_text_gcn, is also available for scenarios where test documents are not included in the training process.

Best used for

Ideal for researchers and developers who need to implement Graph Convolutional Networks for text classification, reproduce academic paper results, and experiment with GCN architectures. Especially valuable for those working in NLP and deep learning research.

Common actions

classify text

implement graph neural networks

reproduce research results

develop NLP models

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Capabilities

Key features

Graph Convolutional Networks
Text classification
Reproduce AAAI 2019 results
Data preparation scripts
Graph building
Model training

Target Audience

professordeveloperresearcher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for running text_gcn?

text_gcn requires Python 2.7 or 3.6 and Tensorflow version 1.4.0 or higher. These are standard requirements for many deep learning projects, making it accessible for users with a typical development environment setup.

Can text_gcn be used with custom datasets?

Yes, the `prepare_data.py` script is provided as an example for preparing your own data. It's important to note that newline characters ('\n') should be removed from your documents or sentences for proper processing.

Is there an inductive version of text_gcn available?

Yes, an inductive version named `fast_text_gcn` is available. This version is designed for scenarios where test documents are not included in the training process, offering a different approach to model evaluation.

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Research & Education › Academic Research AI Agents & Automation › AI Frameworks & Infra

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