People always seem to look at google as though they're doing some special secret magic, but in fact they're really just implementing fairly well-known CS algorithms. They just do it exceptionally well.
That said, I don't think tagging for them would be very simple like you say. For a start they're dealing with multiple languages, probably many languages without any human annotated training corpora. Even for the languages with training data, web pages are difficult to tag & parse because they often contain very 'slack' grammar and domain specific/slang words. The standard English training corpus is the Penn Treebank (Wall Street Journal text), can you imagine trying to read and understand youtube comments if all you'd ever read was the WSJ? Even tagging search queries would be difficult because they're not even sentence fragments you could use viterbi with, they're often just words strung together without any grammar construct at all so you can't rely on the tag order you know from your corpus to help you tag a query.
So I'm very impressed that they're doing any tagging at all, on the scale they're doing it at, and with presumably decent enough results for it to be useful.
But still, all the obvious ways to use POS tagging for IR have been tried, and don't work. POS is only just marginally useful in machine translation at the moment!
This isn't that surprising when you think about it. What POS tags provide is a small clue about grammatical structure. So you're either setting off down that path and trying to understand a sentence as a tree, or you're going to understand a sentence as a sequence of words. Syntax is hard, and most word-word dependencies are between adjacent words.
There's a local maximum at "just use the words", and POS tags don't take you far enough to get past it for many tasks.
I agree, the bag of words / vector space model approach is (from what I've read) hard to beat, especially for a one-size-fits-all tool like google where you can assume nothing about the domain. (Unless they're switching approaches depending on the domain, which would be fascinating)
I suspect we have some miscommunication here.
Experimented with the Brill POS tagger, which I think is a little old now.
I can't speak for the state-of-the-art, but that's certainly what I was taught last year. I studied both computational linguistics and IR -two very different approaches to the same problem. IR seemed to be getting much further in terms of real-world results than linguistic approaches. Although linguistic approaches may seem to offer greater potential.
Also a paper from someone at Google on brute force paraphrase acquisition using billions of sentences combined with some relatively simple rules.
http://www.scribd.com/doc/13863110/The-Unreasonable-Effectiv...