It's usually short for "gold standard", as in the highest level of achievement one measures against to see how good they are. As u/useful said, they are usually data sets/labeled data hand-made by humans with expertise in the field in question. So gold (standard) syntax trees are those curated and labeled by linguists. I haven't read this paper, but I can only assume they mean that training on hand-labeled syntax trees dramatically improves the results of their system over the common practice of letting the system come up with its own labels.

Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora.

A community for discussion and news related to Natural Language Processing (NLP). Natural language processing (NLP) is a field of computer science, artificial intelligence and computational linguistics concerned with the interactions between computers and human (natural) languages, and, in particular, concerned with programming computers to fruitfully process large natural language corpora.

I'm a software engineer with 10 years of experience who recently decided to switch my focus to machine learning. I did the coursera course and did CS231n: Convolutional Neural Networks for Visual Recognition, read up on basic theory, did some image processing networks like VGG, Resnets and most recently trying to get Faster-RCNN to work, so my currently knowledge is ML basics and heavily focussed on ML in the Image domain. I recently landed my first ML job at a company that does mostly NLP, so I lack a lot of knowledge in that domain. I'm currently reading the NLTK book, which has been very approachable in introducing basic concepts in a code-focussed way. So I was wondering if anyone could point me to some good mid to advanced level resources (online courses/videos/books) to get up to speed with where the field is at now, to help me understand current research and more advanced concepts?