[jfriedly]

I read the paper when this first showed up on HN[1]. The most important thing they did was to create a training set with higher granularity in the data than much of anything previously seen. Based on their training set, their algorithm was able to achieve 85% positive/negative accuracy on sentences, but previously state-of-the-art algorithms moved from 80% accuracy up to 83% accuracy when adapted to their training set. While their algorithm appears to be better than anything they tested against, this is fundamentally an incremental improvement, not groundbreaking research. The real win here came from using a better dataset.

[1] http://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf

Edit: formatting

[vivekn]

Will be interesting to play around with that dataset.