Am 06.03.2014 20:47, schrieb Aditya Shah:
@Jo Well I am still unconvinced of your opinion that such the strategy that
I intend to adopt will fail in the long run. I'll give you my reasons for
it. Firstly, we are just talking about Math Spec Languages not generalized
programming languages with complex rules. I have noticed that the rules of
different Math Spec Languages tend to be quite similar differing only in
the syntactic sugar. After all, the functions are quite similar.
Sure... it might work.
Essentially, it comes down to the question how hairy the differences
really are (or become). It's not something that can be determined
beforehand because the devil is in the details; you need to try it out
and see what details are unearthed.
I know that such differences are often massively underestimated.
I do not know whether this is the case here or not.
Secondly, I am not planning to use a hand written one.
Ah ok, I wasn't sure about that.
> Right now sympy uses
hand written parsers which has made the development process of new parsers
a mess because of lack of structure and lack of standardization. All I want
to achieve is a standard by which we will always be able to define new
parsers should the need arise.
Okay, that's a good plan.
Also, I intend to pursue development of NLP parser for sympy(quite
rudimentary) so as to achieve a basic capability to match that of
WolphramAlpha's. I suggest you read the thread (the part where certik
points to the same topic) and give your 2 cents about my approach.
Sigh.
You should not have to develop an NLP parser (Earley/Tomita/GLR/NLP
parsing is essentially all the same).
Just use one of the existing libraries, don't reinvent the wheel (with a
high risk of doing it badly).
Try http://pythonhosted.org/modgrammar/ .
Or http://pages.cpsc.ucalgary.ca/~aycock/spark/ .
Systematic rules (folding letter case, renaming etc.) can be handled in
the lexer or during output generation. I don't know what's the better
approach; I'd try the output side first because lexers tend to know too
little about the context to always make the right decision.
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