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"
Another example:
input ["apple", "Peking", "in summer"] --> target class "Chinese fruit"
input ["tomato", "New York", "in winter"] --> target class "American vegetable"
input ["apple", "Peking", "in winter"] --> target class "Chinese fruit"
input ["tomato", "Peking", "in winter"] --> target class "Chinese vegetable"
Thank you.
I think word-embedding model will do the job: https://towardsdatascience.com/introduction-to-word-embedding-and-word2vec-652d0c2060fa
@ChangyeLi is right and if you want simplest solution I'd suggest you use pre-trained models such as Glove or Word2Vec. Besides, if you have limited words which putting them together as features doesn't make a huge vector, then you can try simple deep neural network with multiple dense layer and a softmax layer as its output.
you can use deep learning, example is given through the code: https://au.mathworks.com/help/deeplearning/examples/create-simple-deep-learning-network-for-classification.html;jsessionid=7018848c980ee0578db631fd32eb
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.
Another improvement: check out PCNN or PCNN+ATT
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