[laretluval]

I agree with your criticism in general, but the lack of outsider-friendly explanation here seems justified because something like a parse tree matcher is more of a tool that's useful inside the NLP research community than for end users. When does an end user ever need to find trees with a particular syntactic structure? On the other hand, it is very useful for debugging parsers, verifying annotation standards in corpora, etc.: things that NLP researchers have to do.

There is some NLP software that does a great job of explaining what it does, how it does it, and why this is useful. http://spacy.io/ comes to mind. Maybe that's the happy exception.

[youngprogrammer]

I would argue that parse tree matching is useful to end users (programmers) who are trying to extract some meaning from a sentence. By matching a parse tree, you can match and extract the contextual information about a sentence, e.g. the subject, action and object. Compare this to the intent matching machine learning approach where you feed a model some sample sentences and manually tag the sentences (e.g. wit.ai). You feed a sentence to this black box and you might get the right intent and context matching but you don't know what's going on and you have no control over the matching. Manually create rules for matching parse trees is a little more work than manually tagging sentences but it allows for more control and transparency over how the matching is done.

[thesoonerdev]

Spacy's website looks good. Thanks for the heads up.

[syllogism]

Unfortunately the two demos 404 at the moment. I hope we can have everything back online soon.