Graph Embeddings for Document Similarity

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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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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.

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

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