Machine Learning on Graphs

Graph Embeddings for Document Similarity you own this product

This project is part of the liveProject series Machine Learning on Graphs for NLP
prerequisites
intermediate Python • basic Graph Theory • intermediate NLP • intermediate Deep Learning • Basic Neo4j
skills learned
converting graph nodes to vectors • dimensionality-reduction • clustering
Sujit Pal
1 week · 4-6 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

Now that one of the data scientists at your publishing company was able to extract citations from the full text of the representative text corpus, your task is to generate new vector embeddings based on the citation graph, then cluster the documents using these embeddings to gain insight from the graph structure.

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:


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

you will learn

In this liveProject, you’ll learn skills, tools, and techniques for determining document similarity using graph embeddings:


  • Representing graph nodes as vectors
  • Applying off-the-shelf machine learning techniques to vectors
  • Using document similarity signals to cluster documents

features

Self-paced
You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
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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.
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