P = precision R = recall F = F-score F-score is just the harmonic mean of precision and recall. https://en.wikipedia.org/wiki/Precision_and_recall https://en.

P = precision R = recall F = F-score F-score is just the harmonic mean of precision and recall. https://en.wikipedia.org/wiki/Precision_and_recall https://en.wikipedia.org/wiki/Precision_and_recall The paper isn't that extraordinary. Sarcasm detection is considered a hard problem. However, if you get a result that's "better" than some other published paper, you usually publish your work. You get (at most) a 5% boost in F-score using embedding features. Word embeddings are easy to work with, so it's not usually difficult to add them in with commonly used NLP features. You can give your model metrics an effortless bump. Also, it looks like there could be a deep learning model that performs better already (https://techxplore.com/news/2016-08-deep-neural-network-approach-sarcasm.html https://techxplore.com/news/2016-08-deep-neural-network-appr...). I've done two deep learning projects at work. I started learning about the techniques this past spring. They're ridiculously good, especially if you have a ton of data. Feature engineering (like in the paper) is usually a laborious process, and it often requires you to be knowledgeable in the problem domain. With neural nets if you have a reasonable architecture and choose sensible data representations (so your backpropagation converges), it can often "just work" without much tweaking.