The title sums it up. Presumably anything that can be done with source-code-generating parser-generators (which essentially hard-code the grammar-to-be-parsed into the program) can be done with a configurable parser (which would maintain the grammar-to-be-parsed soft-coded as a data structure).
I suppose the hard-coded code-generated-parser will have a performance bonus with one less level of indirection, but the messiness of having to compile and run it (or to exec() it in dynamic languages) and the overall clunkiness of code-generation seems quite a big downside. Are there any other benefits of code-generating your parsers that I'm not aware of?
Most of the places I see code generation used is to work around limitations in the meta-programming ability of the languages (i.e. web frameworks, AOP, interfacing with databases), but the whole lex-parse thing seems pretty straightforward and static, not needing any of the extra metaprogramming dynamism that you get from code-generation. What gives? Is the performance benefit that great?
If all you want is a parser that you can configure by handing it grammar rules, that can be accomplished. An Earley parser will parse any context-free language given just a set of rules. The price is significant execution time: O(N^3), where N is the length of the input. If N is large (as it is for many parseable entities), you can end with Very Slow parsing.
And this is the reason for a parser generator (PG). If you parse a lot of documents, Slow Parsing is bad news. Compilers are one program where people parse a lot of documents, and no programmer (or his manager) wants the programmer waiting for the compiler. There's lots of other things to parse: SQL querys, JSON documents, ... all of which have this "Nobody is willing to wait" property.
What PGs do is to take many decisions that would have to occur at runtime (e.g., for an Earley parser), and precompute those results at parser-generation time. So an LALR(1) PG (e.g., Bison) will produce parsers that run in O(N) time, and that's obviously a lot faster in practical circumstances. (ANTLR does something similar for LL(k) parsers). If you want full context free parsing that is usually linear, you can use a variant of LR parsing called GLR parsing; this buys you the convienience of an "configurable" (Earley) parser, with much better typical performance.
This idea of precomputing in advance is generally known as partial evaluation, that is, given a function F(x,y), and knowledge that x is always a certain constant x_0, compute a new function F'(y)=F(x0,y) in which decisions and computations solely dependent on the value of x are precomputed. F' usually runs a lot faster than F. In our case, F is something like generic parsing (e.g., an Earley parser), x is a grammar argument with x0 being a specific grammar, and F' is some parser infrastructure P and additional code/tables computed by the PG such that F'=PG(x)+P.
In the comments to your question, there seems to be some interest in why one doesn't just run the parser generator in effect at runtime. The simple answer is, it pays a significant part of the overhead cost you want to get rid of at runtime.
Related
I'm looking for algorithm to help me predict next token given a string/prefix and Context free grammar.
First question is what is the exact structure representing CFG. It seems it is a tree, but what type of tree ? I'm asking because the leaves are always ordered , is there a ordered-tree ?
May be if i know the correct structure I can find algorithm for bottom-up search !
If it is not exactly a Search problem, then the next closest thing it looks like Parsing the prefix-string and then Generating the next-token ? How do I do that ?
any ideas
my current generated grammar is simple it has no OR rules (except when i decide to reuse the grammar for new sequences, i will be). It is generated by Sequitur algo and is so called SLG(single line grammar) .. but if I generate it using many seq's the TOP rule will be Ex:>
S : S1 z S3 | u S2 .. S5 S1 | S4 S2 .. |... | Sn
S1 : a b
S2 : h u y
...
..i.e. top-heavy SLG, except the top rule all others do not have OR |
As a side note I'm thinking of a ways to convert it to Prolog and/or DCG program, where may be there is easier way to do what I want easily ?! what do you think ?
TL;DR: In abstract, this is a hard problem. But it can be pretty simple for given grammars. Everything depends on the nature of the grammar.
The basic algorithm indeed starts by using some parsing algorithm on the prefix. A rough prediction can then be made by attempting to continue the parse with each possible token, retaining only those which do not produce immediate errors.
That will certainly give you a list which includes all of the possible continuations. But the list may also include tokens which cannot appear in a correct input. Indeed, it is possible that the correct list is empty (because the given prefix is not the prefix of any correct input); this will happen if the parsing algorithm is unable to correctly verify whether a token sequence is a possible prefix.
In part, this will depend on the grammar itself. If the grammar is LR(1), for example, then the LR(1) parsing algorithm can precisely identify the continuation set. If the grammar is LR(k) for some k>1, then it is theoretically possible to produce an LR(1) grammar for the same language, but the resulting grammar might be impractically large. Otherwise, you might have to settle for "false positives". That might be acceptable if your goal is to provide tab-completion, but in other circumstances it might not be so useful.
The precise datastructure used to perform the internal parse and exploration of alternatives will depend on the parsing algorithm used. Many parsing algorithms, including the standard LR parsing algorithm whose internal data structure is a simple stack, feature a mutable internal state which is not really suitable for the exploration step; you could adapt such an algorithm by making a copy of the entire internal data structure (that is, the stack) before proceeding with each trial token. Alternatively, you could implement a copy-on-write stack. But the parser stack is not usually very big, so copying it each time is generally feasible. (That's what Bison does to produce expanded error messages with an "expected token" list, and it doesn't seem to trigger unacceptable runtime overhead in practice.)
Alternatively, you could use some variant of CYK chart parsing (or a GLR algorithm like the Earley algorithm), whose internal data structures can be implemented in a way which doesn't involve destructive modification. Such algorithms are generally used for grammars which are not LR(1), since they can cope with any CFG although highly ambiguous grammars can take a long time to parse (proportional to the cube of the input length). As mentioned above, though, you will get false positives from such algorithms.
If false positives are unacceptable, then you could use some kind of heuristic search to attempt to find an input sequence which completes the trial prefix. This can in theory take quite a long time, but for many grammars a breadth-first search can find a completion within a reasonable time, so you could terminate the search after a given maximum time. This will not produce false positives, but the time limit might prevent it from finding the complete set of possible continuations.
I wish to understand how does a parser work. I learnt about the LL, LR(0), LR(1) parts, how to build, NFA, DFA, parse tables, etc.
Now the problem is, i know that a lexer should extract tokens only on the parser demand in some situation, when it's not possible to extract all the tokens in one separated pass. I don't exactly understand this kind of situation, so i'm open to any explanation about this.
The question now is, how should a lexer does its job ? should it base its recognition on the current "contexts", the current non-terminals supposed to be parsed ? is it something totally different ?
What about the GLR parsing : is it another case where a lexer could try different terminals, or is it only a syntactic business ?
I would also want to understand what it's related to, for example is it related to the kind of parsing technique (LL, LR, etc) or only the grammar ?
Thanks a lot
The simple answer is that lexeme extraction has to be done in context. What one might consider be lexemes in the language may vary considerably in different parts of the language. For example, in COBOL, the data declaration section has 'PIC' strings and location-sensitive level numbers 01-99 that do not appear in the procedure section.
The lexer thus to somehow know what part of the language is being processed, to know what lexemes to collect. This is often handled by having lexing states which each process some subset of the entire language set of lexemes (often with considerable overlap in the subset; e.g., identifiers tend to be pretty similar in my experience). These states form a high level finite state machine, with transitions between them when phase changing lexemes are encountered, e.g., the keywords that indicate entry into the data declaration or procedure section of the COBOL program. Modern languages like Java and C# minimize the need for this but most other languages I've encountered really need this kind of help in the lexer.
So-called "scannerless" parsers (you are thinking "GLR") work by getting rid of the lexer entirely; now there's no need for the lexer to produce lexemes, and no need to track lexical states :-} Such parsers work by simply writing the grammar down the level of individual characters; typically you find grammar rules that are the exact equivalent of what you'd write for a lexeme description. The question is then, why doesn't such a parser get confused as to which "lexeme" to produce? This is where the GLR part is useful. GLR parsers are happy to process many possible interpretations of the input ("locally ambiguous parses") as long as the choice gets eventually resolved. So what really happens in the case of "ambiguous tokens" is the the grammar rules for both "tokens" produce nonterminals for their respectives "lexemes", and the GLR parser continues to parse until one of the parsing paths dies out or the parser terminates with an ambiguous parse.
My company builds lots of parsers for languages. We use GLR parsers because they are very nice for handling complex languages; write the context-free grammar and you have a parser. We use lexical-state based lexeme extractors with the usual regular-expression specification of lexemes and lexical-state-transitions triggered by certain lexemes. We could arguably build scannerless GLR parsers (by making our lexers produce single characters as tokens :) but we find the efficiency of the state-based lexers to be worth the extra trouble.
As practical extensions, our lexers actually use push-down-stack automata for the high level state machine rather than mere finite state machines. This helps when one has high level FSA whose substates are identical, and where it is helpful for the lexer to manage nested structures (e.g, match parentheses) to manage a mode switch (e.g., when the parentheses all been matched).
A unique feature of our lexers: we also do a little tiny bit of what scannerless parsers do: sometimes when a keyword is recognized, our lexers will inject both a keyword and an identifier into the parser (simulates a scannerless parser with a grammar rule for each). The parser will of course only accept what it wants "in context" and simply throw away the wrong alternative. This gives us an easy to handle "keywords in context otherwise interpreted as identifiers", which occurs in many, many languages.
Ideally, the tokens themselves should be unambiguous; you should always be able to tokenise an input stream without the parser doing any additional work.
This isn't always so simple, so you have some tools to help you out:
Start conditions
A lexer action can change the scanner's start condition, meaning it can activate different sets of rules.
A typical example of this is string literal lexing; when you parse a string literal, the rules for tokenising usually become completely different to the language containing them. This is an example of an exclusive start condition.
You can separate ambiguous lexings if you can identify two separate start conditions for them and ensure the lexer enters them appropriately, given some preceding context.
Lexical tie-ins
This is a fancy name for carrying state in the lexer, and modifying it in the parser. If a certain action in your parser gets executed, it modifies some state in the lexer, which results in lexer actions returning different tokens. This should be avoided when necessary, because it makes your lexer and parser both more difficult to reason about, and makes some things (like GLR parsers) impossible.
The upside is that you can do things that would require significant grammar changes with relatively minor impact on the code; you can use information from the parse to influence the behaviour of the lexer, which in turn can come some way to solving your problem of what you see as an "ambiguous" grammar.
Logic, reasoning
It's probable that it is possible to lex it in one parse, and the above tools should come second to thinking about how you should be tokenising the input and trying to convert that into the language of lexical analysis. :)
The fact is, your input is comprised of tokens—whether you like it or not!—and all you need to do is find a way to make a program understand the rules you already know.
Soulver is a great scratch pad for math that allows you to write expressions in a very natural form, which makes it versatile and fun to use in many occasions. There's a short video on the site that displays a lot of its functionality.
I'd like to tackle writing a parser that behaved much as that of that app's. For instance, if you go shopping, you can write a big list like
2 * 1.99 soap + 2.99 cereal + 39.59 organic magic beans
and see, as you type, the sum of what's in the line (46.56).
You can also create variables, such as
March = 2 * 1.99 soap + 2.99 cereal + 39.59 organic magic beans
and reference them in later operations. Other operators, such as 'off' (40% off $200), also exist.
Considering it has some level of sophistication and it should distinguish meaningful terms while ignoring some of the input, what sort of grammar should I be using to represent this little language? I could probably cobble some spaghetti regex together, but I'd honestly like to do something a little better, even if it requires a lot of study from my part. What would you recommend?
A regexp by itself is likely not expressive enough to the job if you want to model real mathematics, e.g., anything with nested parentheses.
Context-free grammars are remarkably expressive. You should learn about Backus Normal Form (BNF), a means for writing down the description of languages as context-free grammars.
You can choose from among many parser generator tools, to convert that grammar into a real parser.
Which specific grammar you write depends on what you want the expressions to mean, and which atoms in the expression really get ignored.
As a practical matter, the way you write the BNF varies from tool to tool, so choosing your parser generator tool first will save you the trouble of rewriting your BNF later.
I know that in some languages (Haskell?) the striving is to achieve point-free style, or to never explicitly refer to function arguments by name. This is a very difficult concept for me to master, but it might help me to understand what the advantages (or maybe even disadvantages) of that style are. Can anyone explain?
The point-free style is considered by some author as the ultimate functional programming style. To put things simply, a function of type t1 -> t2 describes a transformation from one element of type t1 into another element of type t2. The idea is that "pointful" functions (written using variables) emphasize elements (when you write \x -> ... x ..., you're describing what's happening to the element x), while "point-free" functions (expressed without using variables) emphasize the transformation itself, as a composition of simpler transforms. Advocates of the point-free style argue that transformations should indeed be the central concept, and that the pointful notation, while easy to use, distracts us from this noble ideal.
Point-free functional programming has been available for a very long time. It was already known by logicians which have studied combinatory logic since the seminal work by Moses Schönfinkel in 1924, and has been the basis for the first study on what would become ML type inference by Robert Feys and Haskell Curry in the 1950s.
The idea to build functions from an expressive set of basic combinators is very appealing and has been applied in various domains, such as the array-manipulation languages derived from APL, or the parser combinator libraries such as Haskell's Parsec. A notable advocate of point-free programming is John Backus. In his 1978 speech "Can Programming Be Liberated From the Von Neumann Style ?", he wrote:
The lambda expression (with its substitution rules) is capable of
defining all possible computable functions of all possible types
and of any number of arguments. This freedom and power has its
disadvantages as well as its obvious advantages. It is analogous
to the power of unrestricted control statements in conventional
languages: with unrestricted freedom comes chaos. If one
constantly invents new combining forms to suit the occasion, as
one can in the lambda calculus, one will not become familiar with
the style or useful properties of the few combining forms that
are adequate for all purposes. Just as structured programming
eschews many control statements to obtain programs with simpler
structure, better properties, and uniform methods for
understanding their behavior, so functional programming eschews
the lambda expression, substitution, and multiple function
types. It thereby achieves programs built with familiar
functional forms with known useful properties. These programs are
so structured that their behavior can often be understood and
proven by mechanical use of algebraic techniques similar to those
used in solving high school algebra problems.
So here they are. The main advantage of point-free programming are that they force a structured combinator style which makes equational reasoning natural. Equational reasoning has been particularly advertised by the proponents of the "Squiggol" movement (see [1] [2]), and indeed use a fair share of point-free combinators and computation/rewriting/reasoning rules.
[1] "An introduction to the Bird-Merteens Formalism", Jeremy Gibbons, 1994
[2] "Functional Programming with Bananas, Lenses, Envelopes and Barbed Wire", Erik Meijer, Maarten Fokkinga and Ross Paterson, 1991
Finally, one cause for the popularity of point-free programming among Haskellites is its relation to category theory. In category theory, morphisms (which could be seen as "transformations between objects") are the basic object of study and computation. While partial results allow reasoning in specific categories to be performed in a pointful style, the common way to build, examine and manipulate arrows is still the point-free style, and other syntaxes such as string diagrams also exhibit this "pointfreeness". There are rather tight links between the people advocating "algebra of programming" methods and users of categories in programming (for example the authors of the banana paper [2] are/were hardcore categorists).
You may be interested in the Pointfree page of the Haskell wiki.
The downside of pointfree style is rather obvious: it can be a real pain to read. The reason why we still love to use variables, despite the numerous horrors of shadowing, alpha-equivalence etc., is that it's a notation that's just so natural to read and think about. The general idea is that a complex function (in a referentially transparent language) is like a complex plumbing system: the inputs are the parameters, they get into some pipes, are applied to inner functions, duplicated (\x -> (x,x)) or forgotten (\x -> (), pipe leading nowhere), etc. And the variable notation is nicely implicit about all that machinery: you give a name to the input, and names on the outputs (or auxiliary computations), but you don't have to describe all the plumbing plan, where the small pipes will go not to be a hindrance for the bigger ones, etc. The amount of plumbing inside something as short as \(f,x,y) -> ((x,y), f x y) is amazing. You may follow each variable individually, or read each intermediate plumbing node, but you never have to see the whole machinery together. When you use a point-free style, all the plumbing is explicit, you have to write everything down, and look at it afterwards, and sometimes it's just plain ugly.
PS: this plumbing vision is closely related to the stack programming languages, which are probably the least pointful programming languages (barely) in use. I would recommend trying to do some programming in them just to get of feeling of it (as I would recommend logic programming). See Factor, Cat or the venerable Forth.
I believe the purpose is to be succinct and to express pipelined computations as a composition of functions rather than thinking of threading arguments through. Simple example (in F#) - given:
let sum = List.sum
let sqr = List.map (fun x -> x * x)
Used like:
> sum [3;4;5]
12
> sqr [3;4;5]
[9;16;25]
We could express a "sum of squares" function as:
let sumsqr x = sum (sqr x)
And use like:
> sumsqr [3;4;5]
50
Or we could define it by piping x through:
let sumsqr x = x |> sqr |> sum
Written this way, it's obvious that x is being passed in only to be "threaded" through a sequence of functions. Direct composition looks much nicer:
let sumsqr = sqr >> sum
This is more concise and it's a different way of thinking of what we're doing; composing functions rather than imagining the process of arguments flowing through. We're not describing how sumsqr works. We're describing what it is.
PS: An interesting way to get your head around composition is to try programming in a concatenative language such as Forth, Joy, Factor, etc. These can be thought of as being nothing but composition (Forth : sumsqr sqr sum ;) in which the space between words is the composition operator.
PPS: Perhaps others could comment on the performance differences. It seems to me that composition may reduce GC pressure by making it more obvious to the compiler that there is no need to produce intermediate values as in pipelining; helping make the so-called "deforestation" problem more tractable.
While I'm attracted to the point-free concept and used it for some things, and agree with all the positives said before, I found these things with it as negative (some are detailed above):
The shorter notation reduces redundancy; in a heavily structured composition (ramda.js style, or point-free in Haskell, or whatever concatenative language) the code reading is more complex than linearly scanning through a bunch of const bindings and using a symbol highlighter to see which binding goes into what other downstream calculation. Besides the tree vs linear structure, the loss of descriptive symbol names makes the function hard to intuitively grasp. Of course both the tree structure and the loss of named bindings also have a lot of positives as well, for example, functions will feel more general - not bound to some application domain via the chosen symbol names - and the tree structure is semantically present even if bindings are laid out, and can be comprehended sequentially (lisp let/let* style).
Point-free is simplest when just piping through or composing a series of functions, as this also results in a linear structure that we humans find easy to follow. However, threading some interim calculation through multiple recipients is tedious. There are all kinds of wrapping into tuples, lensing and other painstaking mechanisms go into just making some calculation accessible, that would otherwise be just the multiple use of some value binding. Of course the repeated part can be extracted out as a separate function and maybe it's a good idea anyway, but there are also arguments for some non-short functions and even if it's extracted, its arguments will have to be somehow threaded through both applications, and then there may be a need for memoizing the function to not actually repeat the calculation. One will use a lot of converge, lens, memoize, useWidth etc.
JavaScript specific: harder to casually debug. With a linear flow of let bindings, it's easy to add a breakpoint wherever. With the point-free style, even if a breakpoint is somehow added, the value flow is hard to read, eg. you can't just query or hover over some variable in the dev console. Also, as point-free is not native in JS, library functions of ramda.js or similar will obscure the stack quite a bit, especially with the obligate currying.
Code brittleness, especially on nontrivial size systems and in production. If a new piece of requirement comes in, then the above disadvantages get into play (eg. harder to read the code for the next maintainer who may be yourself a few weeks down the line, and also harder to trace the dataflow for inspection). But most importantly, even something seemingly small and innocent new requirement can necessitate a whole different structuring of the code. It may be argued that it's a good thing in that it'll be a crystal clear representation of the new thing, but rewriting large swaths of point-free code is very time consuming and then we haven't mentioned testing. So it feels that the looser, less structured, lexical assignment based coding can be more quickly repurposed. Especially if the coding is exploratory, and in the domain of human data with weird conventions (time etc.) that can rarely be captured 100% accurately and there may always be an upcoming request for handling something more accurately or more to the needs of the customer, whichever method leads to faster pivoting matters a lot.
To the pointfree variant, the concatenative programming language, i have to write:
I had a little experience with Joy. Joy is a very simple and beautiful concept with lists. When converting a problem into a Joy function, you have to split your brain into a part for the stack plumbing work and a part for the solution in the Joy syntax. The stack is always handled from the back. Since the composition is contained in Joy, there is no computing time for a composition combiner.
So I'm doing a Parser, where I favor flexibility over speed, and I want it to be easy to write grammars for, e.g. no tricky workaround rules (fake rules to solve conflicts etc, like you have to do in yacc/bison etc.)
There's a hand-coded Lexer with a fixed set of tokens (e.g. PLUS, DECIMAL, STRING_LIT, NAME, and so on) right now there are three types of rules:
TokenRule: matches a particular token
SequenceRule: matches an ordered list of rules
GroupRule: matches any rule from a list
For example, let's say we have the TokenRule 'varAccess', which matches token NAME (roughly /[A-Za-z][A-Za-z0-9_]*/), and the SequenceRule 'assignment', which matches [expression, TokenRule(PLUS), expression].
Expression is a GroupRule matching either 'assignment' or 'varAccess' (the actual ruleset I'm testing with is a bit more complete, but that'll do for the example)
But now let's say I want to parse
var1 = var2
And let's say the Parser begins with rule Expression (the order in which they are defined shouldn't matter - priorities will be solved later). And let's say the GroupRule expression will first try 'assignment'. Then since 'expression' is the first rule to be matched in 'assignment', it will try to parse an expression again, and so on until the stack is filled up and the computer - as expected - simply gives up in a sparkly segfault.
So what I did is - SequenceRules add themselves as 'leafs' to their first rule, and become non-roôt rules. Root rules are rules that the parser will first try. When one of those is applied and matches, it tries to subapply each of its leafs, one by one, until one matches. Then it tries the leafs of the matching leaf, and so on, until nothing matches anymore.
So that it can parse expressions like
var1 = var2 = var3 = var4
Just right =) Now the interesting stuff. This code:
var1 = (var2 + var3)
Won't parse. What happens is, var1 get parsed (varAccess), assign is sub-applied, it looks for an expression, tries 'parenthesis', begins, looks for an expression after the '(', finds var2, and then chokes on the '+' because it was expecting a ')'.
Why doesn't it match the 'var2 + var3' ? (and yes, there's an 'add' SequenceRule, before you ask). Because 'add' isn't a root rule (to avoid infinite recursion with the parse-expresssion-beginning-with-expression-etc.) and that leafs aren't tested in SequenceRules otherwise it would parse things like
reader readLine() println()
as
reader (readLine() println())
(e.g. '1 = 3' is the expression expected by add, the leaf of varAccess a)
whereas we'd like it to be left-associative, e.g. parsing as
(reader readLine()) println()
So anyway, now we've got this problem that we should be able to parse expression such as '1 + 2' within SequenceRules. What to do? Add a special case that when SequenceRules begin with a TokenRule, then the GroupRules it contains are tested for leafs? Would that even make sense outside that particular example? Or should one be able to specify in each element of a SequenceRule if it should be tested for leafs or not? Tell me what you think (other than throw away the whole system - that'll probably happen in a few months anyway)
P.S: Please, pretty please, don't answer something like "go read this 400pages book or you don't even deserve our time" If you feel the need to - just refrain yourself and go bash on reddit. Okay? Thanks in advance.
LL(k) parsers (top down recursive, whether automated or written by hand) require refactoring of your grammar to avoid left recursion, and often require special specifications of lookahead (e.g. ANTLR) to be able to handle k-token lookahead. Since grammars are complex, you get to discover k by experimenting, which is exactly the thing you wish to avoid.
YACC/LALR(1) grammars aviod the problem of left recursion, which is a big step forward. The bad news is that there are no real programming langauges (other than Wirth's original PASCAL) that are LALR(1). Therefore you get to hack your grammar to change it from LR(k) to LALR(1), again forcing you to suffer the experiments that expose the strange cases, and hacking the grammar reduction logic to try to handle K-lookaheads when the parser generators (YACC, BISON, ... you name it) produce 1-lookahead parsers.
GLR parsers (http://en.wikipedia.org/wiki/GLR_parser) allow you to avoid almost all of this nonsense. If you can write a context free parser, under most practical circumstances, a GLR parser will parse it without further effort. That's an enormous relief when you try to write arbitrary grammars. And a really good GLR parser will directly produce a tree.
BISON has been enhanced to do GLR parsing, sort of. You still have to write complicated logic to produce your desired AST, and you have to worry about how to handle failed parsers and cleaning up/deleting their corresponding (failed) trees. The DMS Software Reengineering Tookit provides standard GLR parsers for any context free grammar, and automatically builds ASTs without any additional effort on your part; ambiguous trees are automatically constructed and can be cleaned up by post-parsing semantic analyis. We've used this to do define 30+ language grammars including C, including C++ (which is widely thought to be hard to parse [and it is almost impossible to parse with YACC] but is straightforward with real GLR); see C+++ front end parser and AST builder based on DMS.
Bottom line: if you want to write grammar rules in a straightforward way, and get a parser to process them, use GLR parsing technology. Bison almost works. DMs really works.
My favourite parsing technique is to create recursive-descent (RD) parser from a PEG grammar specification. They are usually very fast, simple, and flexible. One nice advantage is you don't have to worry about separate tokenization passes, and worrying about squeezing the grammar into some LALR form is non-existent. Some PEG libraries are listed [here][1].
Sorry, I know this falls into throw away the system, but you are barely out of the gate with your problem and switching to a PEG RD parser, would just eliminate your headaches now.