GNN for Document Classification

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prerequisites
intermediate Python • basic PyTorch Geometric • basic NLP and Graph Theory • intermediate deep learning with PyTorch • basic Colab
skills learned
node classification by text embedding signals and citation graph structure signals • build a GNN classifier with PyTorch Geometric
Sujit Pal
1 week · 6-8 hours per week · ADVANCED
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In this liveProject, you’ll implement a Graph Neural Network (GNN). This powerful model will allow you to use the document content from the first liveProject combined with the structure of the citation graph from the second liveProject to build an even more powerful model—one that will predict the sub-field of statistics of each of your customers’ papers.

This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Sujit Pal
Sujit Pal is a data scientist at Elsevier Labs, an advanced technology group within Elsevier. His areas of interest are Information Retrieval (IR), Natural Language Processing (NLP), and Machine Learning (ML). At Elsevier, he has worked on projects on Image Search and Retrieval, Question Answering, Automated Knowledge Graph Construction, and more. He first became aware of the effectiveness of Graph techniques in NLP about two years ago and has had quite a lot of success with it since. He’s active in various Data Science, ML, and IR communities, and has presented at conferences including PyData, ODSC, Haystack, Graphorum, and Spark Summit. Prior to this liveProject series, he co-authored two books on Deep Learning.

prerequisites

This liveProject is for Natural Language Processing (NLP) practitioners who have an intermediate level of knowledge of the Python programming language (especially in the NLP domain) and who are ready to uplevel their NLP skills by applying GNNs for document classification. To begin this liveProject, you’ll need to be familiar with the following:


TOOLS
  • Intermediate Python
  • Intermediate PyTorch
  • Basic PyTorch Geometric
TECHNIQUES
  • basic NLP and Graph Theory
  • intermediate Deep Learning

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