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Well, the subtle point is that the Stanford parser really _is_ a fine choice for a lot of experiments...Even while it's far from state-of-the-art! For researchers outside of NLP, it's often actually worse to have your parser be 2% better than the previous work, for reasons your readers don't care about and you can't easily explain. If your readers have heard of the Stanford parser, and previous work has used it, it's likely a good choice for your experiment. Basically, if people are always using the new hotness outside of NLP, then those non-NLP researchers have to keep learning the new hotness! Ain't nobody got time for that. I do think we're at a good "save point", though, where we should get people updated to the new technologies. Hence the blog post :) As for use-cases, mostly people will use labelled dependency parses, because why not? And they're mostly used inside other NLP research, for instance I've been working on detecting disfluencies in conversational speech, there's increasing work on using this stuff in translation, information extraction, etc. |