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.

  • $\begingroup$ won't be able to classify if there are too many kinds of ids $\endgroup$ Mar 11 '21 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 $\endgroup$ Mar 11 '21 at 2:43

Siamese networks may be useful in your case.




  • $\begingroup$ 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 '21 at 18:55

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