There exists a similar task that is named text classification.
But I want to find a kind of model that the inputs are keyword set. And the keyword set is not from a sentence.
For example:
input ["apple", "pear", "water melon"] --> target class "fruit"
input ["tomato", "potato"] --> target class "vegetable"
Thank you.
Hi,
are the keywords in your set independent? If yes, then probably Naive Bayes is the best solution for your. If not, it will give you probably a good solutions.
Hope that help a bit.
Use the segment embedding (idea from BERT) for the origin text classification model.
For example:
input ["apple", "Peking", "in summer"] += segment emb [1,2,3,3,0]
input ["tomato", "New York", "in winter"] += segment emb [1,2,2,3,3]
where 1,2,3 are something like the data source type for input.
Useful code for text classification: https://au.mathworks.com/help/deeplearning/examples/create-simple-deep-learning-network-for-classification.html;jsessionid=7018848c980ee0578db631fd32eb
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