I think have a solid ML understanding can serve you well if you are doing NLP.

* Good Freely Available Textbooks on Machine Learning? http://metaoptimize.com/qa/questions/186/

Besides that, I guess it depends specifically on what NLP tasks you are looking at. Can you tell me more?

it's simple enough to get running, fast, and outperforms the state of the art of a couple of years ago.

My main proposal is to post a "How do I build X" question on MetaOptimize. (I don't know any place on the web where experienced NLP people chat, besides MetaOptimize, but I would be interested to hear other options.) NLP isn't really very hard once you know what you're doing, it's just that as an inexperienced person you will spend a lot of time going down dead ends and blind alleys, and there are a lot of pitfalls to avoid. So getting the advice of experienced people, right from the beginning, is the best way to do this.

See also:

* What are the best resources to use when starting machine learning for an experienced programmer? http://metaoptimize.com/qa/questions/334/

* How do I get started understanding statistics & data analysis? http://metaoptimize.com/qa/questions/154/

* New to data mining - where to start? http://metaoptimize.com/qa/questions/362/

The nltk tools them-selves might not always be state of the art (e.g. compare with the java libs from the NLP dept in Stanford) but at least you will get to know all the major concepts.

Other interesting book, although not completely finished yet: http://www.manning.com/ingersoll/ Taming Text by Grant Ingersol, Thomas Morton and Drew Farris involved in the Apache Lucene / Mahout and OpenNLP communities.