Three-Project Series

Machine Learning on Graphs for NLP you own this product

prerequisites
intermediate Python • basic NLP and Graph Theory • intermediate deep learning with PyTorch • Basic Neo4j • Basic Pytorch Geometric • Basic Colab
skills learned
convert text into graphs • explore centrality algorithms for clustering using Neo4j • converting graph nodes to vectors • node classification by text embedding signals and citation graph structure signals • build a GNN classifier with PyTorch Geometric
Sujit Pal
3 weeks · 5-7 hours per week average · ADVANCED

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Welcome to the publishing industry! Imagine you work for a company that publishes scientific articles. Your task is to categorize these articles into different subject areas, empowering the company to gain a better understanding of their client base and serve them more effectively. In this liveProject series, you’ll use various graph techniques to look at the content in different ways. You’ll attempt to impute a graph structure from document similarity, then attempt to categorize the documents using graph embeddings generated from the provided citation graph. Finally, you’ll combine content signals from the text and graph signals from the citation graph to categorize these articles into a small set of subject areas.

These projects are designed for learning purposes and are not complete, production-ready applications or solutions.

book resources

When you start each of the projects in this series, you'll get full access to the following book for 90 days.

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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

These liveProjects are 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 graph-based tools to their text corpora. To begin these liveProjects, you’ll need to be familiar with the following:


TOOLS
  • Intermediate Python
  • Basic SpaCy, Neo4j database, and the Neo4j Graph Data Science (GDS) library
  • Intermediate Pytorch
  • Basic Pytorch Geometric
TECHNIQUES
  • Intermediate linear algebra
  • Intermediate Deep Learning
  • Intermediate NLP
  • Basic Graph Theory

you will learn

In this liveProject series, you’ll learn skills, tools, and techniques for applying powerful graph-based tools to text corpora for effective NLP:


  • Converting document text into vectors
  • Computing similarities between documents
  • Determining thresholds for connecting documents
  • Generating graph analytics
  • Applying graph algorithms to the graphs
  • Representing graph nodes as vectors
  • Node classification by text embedding signals and citation graph structure signals
  • Applying off-the-shelf machine learning techniques to vectors
  • Using document similarity signals to cluster documents
  • Converting node and edge data provided into a PyTorch Geometric Dataset
  • Building the GNN classifier
  • Training and evaluating the classifier

features

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Project roadmap
Each project is divided into several achievable steps.
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For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.