In the paper UNITN: Training Deep Convolutional Neural Network for Twitter Sentiment Classification, as the name suggests, a CNN architecture for sentiment classification is being introduced:
I am currently trying to reproduce these results but I am new to CNN hence I don't really understand a few basic things here. My main question is about those different filters F and how they actually look like.
In image recognition such filters may be just some matrices for edge detection etc. but here we are working with word embeddings. Unfortunately the paper does not explain how they came up with their filters. One thing I could imagine is e.g. to first learn an auto encoder and use the weights of the encoder as a filter. However, as you can see from the picture there are multiple filters being used which does not support this theory because why would there be multiple filters then?
Hopefully somebody can shed some light into this.