This answer is more a story than a comprehensive answer, but I used a mix of Haskell, Python and C++ for my dissertation in computational linguistics, as well as several C and Java tools that I didn't write. I found it simplest to run everything as a separate process, using Python as glue code to start the Haskell, C++ and Java programs.
The C++ was a fairly simple, tight loop that counted feature occurrences. Basically all it did was math and simple I/O. I actually controlled options by having the Python glue code write out a header full of #define
s and recompiling. Kind of hacky, but it worked.
The Haskell was all the intermediate processing: taking the complex output from the various C and Java parsers that I used, filtering extraneous data, and transforming it the simple format the C++ code expected. Then I took the C++ output and transformed it into LaTeX markup (among other formats).
This is an area that you would expect Python to be strong, but I found that Haskell makes manipulation of complex structures easier; Python is probably better for simple line-to-line transformations, but I was slicing and dicing parse trees and I found that I forgot the input and output types when I wrote code in Python.
Since I was using Haskell a lot like a more-structured scripting language, I ended up writing a few file I/O utilities, but beyond that, the built in libraries for tree and list manipulation sufficed.
In summary, if you have a problem like mine, I would suggest C++ for the memory-constrained, speed-critical part, Haskell for the high-level transformations, and Python to run it all.