You're right that it is an opportunity for attention. What seems to actually happen is that a bunch of people who really don't know what they're doing get a bunch of publicity, recruits, and potentially funding and tech-transfer, while we're sitting here with working systems running in production, not getting much. If you look at AI papers that try to touch programs, they have a tendency to not even cite work from the PL community that does the exact same thing but better. It's kinda like how if you Google "fitness," you're guaranteed to get really bad advice for all your results -- the people who actually know about fitness have lost the PR battle.
In short, you can say "it's just a label," but that's not a reason not to fight the battle over words.
Also, NLP is at the very least closely aligned with "AI" research bit traditionally and looking at current trends.
I do get touchy when people label this kind of work "machine learning"
Don't ;) (Seriously - it's just a label. Embrace the attention)