[new resource]: Clustering and Visualising Documents using Word Embeddings

[charlottejmc]

Title of the resource

Clustering and Visualising Documents using Word Embeddings

Resource type

External Resource

Authors, editors and contributors

Jonathan Reades, Jennie Williams, Alex Wermer-Colan, Quinn Dombrowski, Barbara McGillivray

Topics (keywords)

DH, Open Education, Open Access, data visualisation, machine learning, python, network analysis

Learning outcomes

After completing this lesson, you will be able to:

• Appreciate the ‘curse of dimensionality’ and understand why it is important to text mining
• Use (nonlinear) dimensionality reduction to reveal structure in corpora
• Use hierarchical clustering to group similar documents within a corpus

Abstract

This lesson uses word embeddings and clustering algorithms in Python to identify groups of similar documents in a corpus of approximately 9,000 academic abstracts. It will teach you the basics of dimensionality reduction for extracting structure from a large corpus and how to evaluate your results.

[VickyGarnett]

Hi @charlottejmc - these people are all in the system, so you can continue to draft the resource :)