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
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
- 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.