4 ms·
We've been applying convnets to text for a while, though I'm not sure what you mean by generalizable. There's a few other major techniques (other than convnets
by singhrac 10y ago
We've been applying convnets to text for a while, though I'm not sure what you mean by generalizable.
There's a few other major techniques (other than convnets) that are important, like RNNs in general.
- deleted 10y ago[deleted]
- rspeer 10y agoYeah, I should not make such sweeping statements. (Or maybe I should, because it's a great way to find out what other people's perspective is when they show up to correct me.) My concern about the uses of convnets on text that I've seen is that I don't think they can deal with little things like the word "not". (The Stanford movie review thing can definitely handle the word "not", but that's different.) I'm unconvinced as of yet that we're convolving over the right thing. But maybe the right thing is on the way, especially when Google gave us a pretty good parser. And maybe the right thing involves other things like RNNs, sure. I guess image recognition could have similar cases, and the results just look more impressive to me because I work with text and not with images.
- nl 10y agoLSTMs can handle negation. See http://k8si.github.io/2016/01/28/lstm-networks-for-sentiment-analysis-on-tweets.html http://k8si.github.io/2016/01/28/lstm-networks-for-sentiment... for a pretty nice example. I think there are some papers out of the IBM Watson group on question answering where they use ConvNets. I don't remember looking a the negation case specifically, but Question Answering generally has cases where that is important.