Let's ask a rhetorical question: Who'd use this kind of information? My answer: It's mostly search/listing apps. The biggest merit of tagging/parsing for now i

Let's ask a rhetorical question: Who'd use this kind of information? My answer: It's mostly search/listing apps. The biggest merit of tagging/parsing for now is that you can search the sentences who might possibly refer to some specific subway line. Toward this goal, you don't need an exact interpretation of each phrase. What you'd need is a high-level function that asks if "sentence X".refers_to("Blue Line") and the API should be designed for this kind of goals in mind. I think the current premise that you can/should get a exact parse/tagging for a given text is kinda misleading, because an interpretation of natural language is fundamentally vague. IMO, we should go after the performance of these high-level tasks (e.g. searching or translation) rather than microscopic accuracy. It sucks that we wouldn't be able to parse the text once for all and cache the canonical representation for future use, and we'd always have to run the matching API or some sort. But I think it's kinda the nature of NLP.