MaartenGr/VLAC
Vectors of Locally Aggregated Concepts
This tool helps researchers and data scientists analyze document collections by converting text into rich numerical representations. It takes raw text documents and pre-trained word embeddings, then processes them to produce detailed 'concept vectors' for each document. These vectors can then be used for tasks like document classification or similarity analysis.
No commits in the last 6 months. Available on PyPI.
Use this if you need a sophisticated way to represent the thematic content of documents as numerical data for machine learning models, going beyond simple keyword counts.
Not ideal if you're looking for a simple keyword search tool or don't have pre-trained word embeddings for your language.
Stars
13
Forks
5
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jun 17, 2024
Commits (30d)
0
Dependencies
2
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