You say that you need to 'parse' each sentence. You probably already know this, but just to be explicit, in NLP, the term 'parse' usually means to recover some hierarchical syntactic structure. The most common types are constituent structure (e.g., via a context-free grammar) and dependency structure.
If you need hierarchical structure, I'd recommend you consider just starting with a parser. Most parsers I'm aware of include POS tagging during parsing, and may provide higher accuracy tagging than finite-state POS taggers (Caveat - I'm much more familiar with constituent parsers than with dependency parsers. It's possible some or most dependency parsers would require POS tags as input).
The big downside to parsing is the time complexity. Finite-state POS taggers often run at thousands of words per second. Even greedy dependency parsers are considerably slower, and constituent parsers generally run at 1-5 sentences per second. So if you don't need hierarchical structure, you probably want to stick with a finite-state POS tagger for efficiency.
If you do decide you need parse structure, a few recommendations:
I think the Stanford parser suggested by @aab includes both a constituent parser and a dependency parser.
The Berkeley Parser ( http://code.google.com/p/berkeleyparser/ ) is a pretty well-known PCFG constituent parser, achieves state-of-the-art accuracy (equal or superior to the Stanford parser, I believe), and is reasonably efficient (~3-5 sentences per second).
The BUBS Parser ( http://code.google.com/p/bubs-parser/ ) can also run with the high-accuracy Berkeley grammar, and improves efficiency to around 15-20 sentences/second. Full disclosure - I'm one of the primary researchers working on this parser.
Warning: both of these parsers are research code, with all the problems that engenders. But I'd love to see people actually using BUBS, so if it's of use to you, give it a try and contact me with problems, comments, suggestions, etc.
And a couple Wikipedia references for background if needed:
