@Simon Elliston Ball is right, there's a huge variety of options for NLP as there are many niches for natural language processing. Keep in mind that NLP libraries rarely directly solve business solutions directly. Rather, they give you the tools to build a solution. Often this is segmenting free text into chunks suitable for analysis (e.g. sentence disambiguation), annotating free text (e.g. part of speech tagging), converting free text to a more structured form (e.g. vectorization). All of these are tools that are useful in processing text, but are insufficient by themselves. These tools help you convert free, unstructured text into a form suitable as input into a normal machine learning or analysis pipeline (i.e. classification, etc.). I suppose the one exception to this that I can think of is sentiment analysis..that is a properly valuable analytic in and of itself. Also, keep in mind the license for some of these libraries are not as permissive as Apache (e.g. CoreNLP is GPL with the option to purchase a license for commercial use).
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