Most frequently tools or programming language for implementation text processing and nlp algorithms in academic papers and journals
My personnal opinion is that such comparisons usually make little
sense because they can be too dependent on hidden specificity of tools
or programming languages, or programming style and its interaction
with those specificities.

In my opinion, the only proper way to compare techniques is by a precise knowledge of
the interpretation machines. If you are comparing different algorithms
intended to achieve the same purpose, you want to run them on some
kind of unbiased abstract machine, providing some application
meaningful primitives than can be used to implement on the same
footing all the algorithmic variations.

Then you can actually count elementary computation costs over some
chosen benchmark.

For example, I have seen this type of work done for various parsing
algorithms, using a standardized PDA definition.

Making a slightly better tool than what exist for problem X in
language Y is seldom worth publishing, unless there is some more
abstract consideration of some theoretical work that shows that you
are bringing something to the field, rather than just being a better
hacker. And then the tool or language support does not matter so much,
as long as you can compare with some known work, and explain credibly
why your version works better.

Of course, if you actually manage to get major improvements, undisputable ones, then you have a much better chance of being published. But the the technical support you use will matter even less.