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.
When you start your liveProject, you get full access to the following books for 90 days.
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:
- Intermediate Python
- Basic SpaCy, Neo4j database, and the Neo4j Graph Data Science (GDS) library
- 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
- 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
- While within the liveProject platform, get help from other participants and our expert mentors.
- Compare with others
- 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.