1
$\begingroup$

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.

$\endgroup$
2
  • $\begingroup$ won't be able to classify if there are too many kinds of ids $\endgroup$
    – Dee
    Mar 11, 2021 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 $\endgroup$
    – Dee
    Mar 11, 2021 at 2:43

1 Answer 1

0
$\begingroup$

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

$\endgroup$
1
  • $\begingroup$ 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, 2021 at 18:55

You must log in to answer this question.

Not the answer you're looking for? Browse other questions tagged .