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.
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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.