I've worked in the NLP research area (mostly with statistical metrics), and I can safely say that 80% (precision) is the empirical threshold that most metrics a

I've worked in the NLP research area (mostly with statistical metrics), and I can safely say that 80% (precision) is the empirical threshold that most metrics are able to reach quite easily. The threshold between 80% and 90% starts to get difficult and above that you have to do some tweaking to adapt to the specifics of your problem. With that said, your values do seem to be in line with what I consider to be easily reachable, so it kind-of depends on how much work you need to do with the neural networks to extract those keywords. I'm not very knowledgeable on how NN are applied to this field, but I'm assuming that a drawback of that approach is that it may resemble a black-box in the sense that it may be hard to tweak the internals. I prefer statistical metrics because they seem more simple to derive. For instance, you can think of things like "a relevant keyword is usually related with (or closer to) other relevant keywords" and you can test that hypothesis only by counting distances between words. This is what I've done in 2012 with quite good values, you can check the paper here: http://www.sciencedirect.com/science/article/pii/S1877050912001251 http://www.sciencedirect.com/science/article/pii/S1877050912...