Sorting words into categories in Python
for deep learning to work on this you would have to develop a large dataset, most likely manually. the largest natural language processing dataset was, in fact, created manually.
BUT even if you were able to find a dataset which a model could learn off. THEN a model such as gradient boosted trees would be one, amongst others, that would be well suited to multi-class classification like this. A classic library for this is xgboost.
I have about 3,000 words and I would like to group them into about 20-50 different categories. My words are typical phrases you might find in company names. "Face", "Book", "Sales", "Force", for example.
The libraries I have been looking at so far are pandas and scikit-learn. I'm wondering if there is a machine-learning or deep-learning algorithm that would be well suited for this?
The topics I have been looking are Classification: identifying which category an object belongs to, and Dimensionality Reduction: reducing the random number of variables to consider.
When I search for putting words into categories on Google, it brings up kids puzzles such as "things you do with a pencil" - draw. Or "parts of a house" - yard, room.