Machine Learning on Graphs

Graph Structure in Text Data you own this product

This project is part of the liveProject series Machine Learning on Graphs for NLP
prerequisites
intermediate Python • basic NLP and Graph Theory • Basic Neo4j
skills learned
convert text into graphs • explore centrality algorithms for clustering using Neo4j
Sujit Pal
1 week · 6-8 hours per week · ADVANCED
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liveProject This project is part of the liveProject series Machine Learning on Graphs for NLP liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
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Look inside

Imagine you work for a company that publishes scientific articles created by its customers—primarily researchers and scientists in the field of statistics—whose volume of research has grown so large that it’s not possible for your company to read every paper nor for the researchers to stay on top of everything happening in the field. Your task is to use automated means to find some structure in the collection of scientific articles so that the company can more easily home in on the researchers’ interests and help them research more effectively. To do that, you’ll generate document embeddings, use them to impute a document graph, and execute graph algorithms against this graph in order to generate insights from it.

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

book resources

When you start your liveProject, you get full access to the following books for 90 days.

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 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
TECHNIQUES
  • Intermediate linear algebra
  • Basic NLP and Graph Theory
  • Intermediate NLP

you will learn

In this liveProject, 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

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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.
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