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I have a dataset of texts, each text was identified with an ID number. I would like to do a prediction by finding the best match ID number for upcoming new texts. To use multi text classification, I am not sure if this is the right approach since there is only one text for most of ID numbers. In this case, I wouldn't have any test set. Can up-sampling help? Or is there any other approach than classification for such a problem?

The data set looks like this:

id1 'text1', id2 'text2', id3 'text3', id3 'text4', id3 'text5', id4 'text6', . . id200 'text170'

I would appreciate any guidance to find the best approach for this problem.

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  • $\begingroup$ won't be able to classify if there are too many kinds of ids $\endgroup$ – datdinhquoc Mar 11 at 2:40
  • $\begingroup$ and theoretically, won't be able to classify, if the data are singlesample --to--> multiple ids; it must be manysamples --to--> single id $\endgroup$ – datdinhquoc Mar 11 at 2:43
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Siamese networks may be useful in your case.

http://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf

https://en.wikipedia.org/wiki/Siamese_neural_network

https://link.springer.com/protocol/10.1007%2F978-1-0716-0826-5_3

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  • $\begingroup$ Thank you very much! It seems really useful. I hope it works on my text data as well considering the texts in my datasets are kind of messy complaint data. $\endgroup$ – Fara Feb 8 at 18:55

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