Unsupervised guided text classification with NN
Hi, everyone!Could you tell me, please, are there any techniques to tell NN which classes to categorize texts into, when there is no labeled training set or it is very small (1-2 instance for each class)? For example, is it possible to give some key words for each class, so that NN clusterized texts accordingly? Otherwise, NN produces classes which are of no interest to me.My task at hand, is to classify texts on 1500 predefined categories. I was able to do it with GLDAGuided Latent Dirichlet Allocation, but I believe that I can achieve better results using NN.I will be more than happy if you share links to models/articles or your thoughts. Thanks in advance.
2022-03-29T10:17:42Z
I feel like you can usezero shot text classification modelsto label your data, I don’t know if 1500 categories is too much though. Another idea: I recently came across this blog post onusing BERT for topic modelling(it’s like an extension of using embeddings for topic modelling). The author of the blog post is the owner of a package calledBERTopicwhich is something you might use. It’s based ontransformers.
2022-04-01T12:15:09Z
Merve, thanks for your suggestions! It took me some time to check if those models are applicable for my task. And I am certain now that I will give them a try.In addition, I am considering to try other two methods:Tune some sentiment-analysis BERT model, to make it predict classes instead. Though I am in doubt that it will give good results, since I have much much more classes than emotional states it operates with.Try active learning, with some smart schemes to ease the process of assigning one of 1.5k labels to text for assessors. This option is costly.
2022-04-02T19:19:41Z
If your labeled data are not enough, first you can try data augmentation. You can also think about self-supervised learning. It may help you.