# Picking the right NLP model to tag words from a dataset

As the title suggests, I am posting here in the hope someone could direct me towards NLP models for tagging words.

To be more concrete, here is what I wish to do. I would like to build a flashcard application using an NLP model that would tag/categorize words. So let us imagine I have a CSV file with items made of one question (in English) and one answer (in French):

+----------------------------
| plane       | avion       |
+-------------+-------------+
| chopsticks  | baguettes   |
+-------------+-------------+
| airport     | aéroport    |
+-------------+-------------+


The idea is that the learners would pick a contextual deck (in this example, a deck related to travelling with planes). That deck would be generated by a tag "airport" made by the machine learning algorithm.

And thus, is there any good models I should look to?

Edit:

After much research, I came across NLU which meets many of the requirements I have described above. If you are interested, please have a look at those links: What is NLP technique to generalize manually created rules in text? and NLP algorithms for categorizing a list of words with specific topics, as well as this repo: Probase-Concept

You could look into sequence-to-sequence models. These model can be used to "translate" text. There is a nice example in Keras, where you can straight away build a translation model for English -> French.