Are there existing parser algorithms (similar to LALR, SLR and LL) that can parse a single input, not just multiple inputs, in parallel?
Edit: Sorry, I wasn't really looking for research papers, more like, "There are compiler-compilers that generate concurrent parsers" or "This compiler for this language parses it in parallel"- real world examples.
There aren't any well known ones :-}
Much of the reason is the problem is described as parsing a string, presenting to the parser one token a time. That makes the problem sequential by definition, ugh.
One could imagine presenting the array of tokens to some parser all at once, and then have the parser parse substrings at various points across the array, stitching compatible trees for substrings together. The stitching process is likely to be complicated, but might be manageable if driven by an L(AL)R [better, a GLR] parser that swallowed nonterminals left-to-right after most of the parse trees for substrings were produced; think of this an an "accumulator".
[Shades, a quick Google search produces a 1990 Japanese paper on doing parallel GLR with what amounts to parallel Prolog]
You now have the problem of producing the array of tokens magically in parallel. Now you need a parallel lexer :-}
EDIT June 2013: I finally remembered McKeeman's 1982 paper on parallel LR parsing.
I'm working on a deterministic context free parsing algorithm with O(N) work complexity, O(log N) time complexity, fine-grained parallelism that is proportional to the length of the input string, and equivalent behavior to a LR parser. I'll be submitting it for peer review shortly.
The main idea is to process each character in the input stream independently, assume that it could match any rule, then piece together neighboring groups of characters until they unambiguously match a single rule. Once a rule is matched, it is filtered out by the algorithm. After all rules are matched, tokens are gathered together into a sequence.
There is some complexity involved in handling tokens with wildcards that could partially nest, and a post-processing step is needed to handle these and maintain the worst-case O(log N) complexity. This step probably is not needed in practice.
Related
I'm currently studying compiler's and am on the topic of "Chomsky Hierarchy and the 4 languages." But it beats me as to what the practical purpose of all this is?
It'd be great if I could see real-life examples of the 4 grammars: Unrestricted, CSG, CFG, Regular Grammer come to play.
I found online that Chomsky hierarchy along with the 4 grammars is used to evaluate proposals within cognitive science but this goes way over my head. It'd be great if someone could break it down for me, thanks a lot!
There is no practical value. That's the whole point.
Let me try to break that down a bit. It's useful to remember that Chomsky is a linguist --someone who studies human languages-- and that he was writing in the late 1950s when computational theory was not as well-developed as it is today. (To put it mildly.) His goal was to find a mathematical model which could provide some insights into the mechanisms by which human beings generate and understand sentences, and he took as his starting point a particular simple model of sentence generation.
In this model, a grammar is a function F, which transforms elements from an arbitrary sequence of symbols from some alphabet onto another sequence of symbols from the same alphabet. F is defined by a finite set of pairs (called productions) α → β. We then say that F(ω) = F(ζ) if the definition of F contains some pair α → β such that α is a substring of ω and ζ is the result of substituting a single instance of α in ω with β.
That's not very interesting in and of itself; we make that into a full language by starting with some designated starting sequence, normally represented as the single symbol S, and repeatedly apply F as many times as is necessary. (In all interesting grammars, the set so constructed is infinite, so it cannot actually be constructed. But we can imagine proceeding from the starting point until we find the sentence we wanted to generate.)
The problem with this model is that it can be used to describe an arbitrary Turing Machine. Or, if you like, an arbitrary computer program, although the equivalence is easier to see with a Turing Machine. In other words, it is at least theoretically possible to construct a finite grammar which will recognise strings consisting of the description of a Turing Machine (i.e., a program written in some programming language) followed by an input and an output only if the Turing Machine applied to the input would produce the output. In other words, there exists (in the mathematical sense) a grammar of this form which is computationally equivalent to a general purpose computer.
Unfortunately, that's not actually very useful if our goal is to understand sentences, because there is actually no algorithm for running computers backwards. The best we can do as a general solution is to enumerate all possible inputs and run the program on each of them until we find the output we hoped for. And that doesn't actually work because there is no limit to the amount of time the program might take to produce an output and no way to even know if the program will eventually come to an end. (This is called the "halting problem".) So we might get stuck on some possible input, and we'll never know if some other input might have produced the desired output.
As a result, we cannot tell whether the provided input was "grammatical", that is, whether it conformed to the grammar provided. And that's not just the case with the particular grammar we built to emulate Turing Machines. It means that we have no confidence that we can recognise sentences from any arbitrary grammar, and even if we stumble upon an answer, we have no way to limit how much time it might take to get there.
Clearly, this is not how human beings understand each other. So if it is to serve a practical purpose, we must restrict the possible grammars in some way to make them computationally feasible.
On the other end of the spectrum, a lot was known about finite-state machines. A finite-state machine is a Turing Machine without a tape; that is to say, it is simply a finite collection of states. In each state, the machine reads a single input symbol and uses it to decide what the next state will be. It turns out that finite-state machines can be modelled using a grammar (as above) restricted to very simple productions, each of which is either of the form A → a B or A → a, where a is a symbol from the "terminal alphabet" (that is, a word) and A and B are single grammatical symbols. These grammars are called "regular grammars" and they are computationally equivalent to what mathematicians call "regular expressions" (which are a small subset of what is recognised by "regex" libraries, but that's a whole other discussion).
Regular grammars are easy to parse. All that is needed to is trace through the state machine, so it can be done without backtracking in time proportional to the length of the input. But regular grammars are far too weak to be able to represent human language, or even most computer languages. As a simple example, algebraic expressions with parentheses cannot be recognised with a regular grammar (or with a finite-state machine) because there is no way to count the parenthesis depth; the finite-state machine has no memory at all (other than knowing which state it is in, and there are only a finite number of states).
So unrestricted grammars are too powerful to parse and regular grammars are too weak to be useful. (Useful for complex parsing problems, that is. There are certain applications for regular expressions, but parsing complete computer programs is not one of them.)
The next step, then, was to try to find a restriction on grammars which was still powerful enough to represent human language without being so powerful that parsing became impossible.
That, finally, is the origin of Chomsky's hierarchy. Between the two extremes described above (type 0 and type 3 grammars), Chomsky proposed two possible intermediate restrictions -- type 1 and type 2 grammars -- and proved a number of important properties about each of them.
While this work turned out to be fundamental in the development of formal language theory, it cannot really be said to have answered the question Chomsky started with. Type 2 grammars -- context-free grammars -- are indeed computationally tractable; they can be parsed with simple algorithms in polynomial time, and can represent a large number of useful languages. But they are still too weak to represent human language. In particular, context-free grammars cannot represent a language as simple as "all strings which contain two instances of the same substring". Type 1 grammars -- context-sensitive grammars -- can probably represent any useful language, and are not quite as unruly as unrestricted grammars, but they are still too powerful to parse. (Since the derivation steps in a context-sensitive grammar never get shorter, it is possible to enumerate all possible derivations from a starting point in order by length, which means that you can decide whether a sentence is generated by the grammar without running into the halting problem. But that's as good as it gets; that procedure takes exponential time and is not remotely feasible for non-trivial inputs.)
In the six decades since Chomsky published his seminal papers, a lot of work has been done to try to find useful intermediate restrictions between type 1 and type 2 grammars. And there has been a lot of useful study into algorithms for parsing context-free languages, which is of enormous utility in building compilers. All of this builds on the crucial work done by Chomsky and the other computational theorists whose work he built on -- Markov, Turing, Church and Kleene, just to name a few worthy of study. But Chomsky's original project remains unsolved.
So if your goal is to build a simple parser for a programming language, the Chomsky hierarchy is probably just an interesting footnote. But if you are interested in the academic study of formal language theory, there are still lots of interesting unsolved problems to work on.
Is there an online algorithm which converts certain grammar to the most efficient parser possible?
For example: SLR/LR(k) such as k>=0
For the class of grammars you are discussing (xLR(k)), they are all linear time anyway, and it is impossible to do sublinear time if you have to examine every character.
If you insist on optimizing parse time, you should get a very fast LR parsing engine. LRStar used to be the cat's meow on this topic, but the guy behind it got zero reward from the world of "I want it for free" and pulled all instances of it off the net. You can settle for Bison.
Frankly most of your parsing time will be determined by how fast your parser can process individual characters, e.g., the lexer. Tune that first and you may discover there's no need to tune the parser.
First let's distinguish LR(k) grammars and LR(k) languages. A grammar may not be LR(1), but let's say, for example, LR(2). But the language it generates must have an LR(1) grammar -- and for that matter, it must have an LALR(1) grammar. The table size for such a grammar is essentially the same as for SLR(1) and is more powerful (all SLR(1) grammars are LALR(1) but not vice versa). So, there is really no reason not to use an LALR(1) parser generator if you are looking to do LR parsing.
Since parsing represents only a fraction of the compilation time in modern compilers when lexical analysis and code generation that contains peephole and global optimizations are taken into consideration, I would just pick a tool considering its entire set of features. You should also remember that one parser generator may take a little longer than another to analyze a grammar and to generate the parsing tables. But once that job is done, the table-driven parsing algorithm that will run in the actual compiler thousands of times should not vary significantly from one parser generator to another.
As far as tools for converting arbitrary grammars to LALR(1), for example (in theory this can be done), you could do a Google search (I have not done this). But since semantics are tied to the productions, I would want to have complete control over the grammar being used for parsing and would probably eschew such conversion tools.
We are already aware that the parsing time of Stanford Parser increases as the length of a sentence increases. I am interested in finding creative ways in which we prune the sentence such that the parsing time decreases without compromising on accuracy. For e.g. we can replace known noun phrases with one word nouns. Similarly can there be some other smart ways of guessing a subtree before hand, let's say, using the POS Tag information? We have a huge corpus of unstructured text at our disposal. So we wish to learn some common patterns that can ultimately reduce the parsing time. Also some references to publicly available literature in this regards will also be highly appreciated.
P.S. We already are aware of how to multi-thread using Stanford Parser, so we are not looking for answers from that point of view.
You asked for 'creative' approaches - the Cell Closure pruning method might be worth a look. See the series of publications by Brian Roark, Kristy Hollingshead, and Nathan Bodenstab. Papers: 1 2 3. The basic intuition is:
Each cell in the CYK parse chart 'covers' a certain span (e.g. the first 4 words of the sentence, or words 13-18, etc.)
Some words - particularly in certain contexts - are very unlikely to begin a multi-word syntactic constituent; others are similarly unlikely to end a constituent. For example, the word 'the' almost always precedes a noun phrase, and it's almost inconceivable that it would end a constituent.
If we can train a machine-learned classifier to identify such words with very high precision, we can thereby identify cells which would only participate in parses placing said words in highly improbable syntactic positions. (Note that this classifier might make use of a linear-time POS tagger, or other high-speed preprocessing steps.)
By 'closing' these cells, we can reduce both the the asymptotic and average-case complexities considerably - in theory, from cubic complexity all the way to linear; practically, we can achieve approximately n^1.5 without loss of accuracy.
In many cases, this pruning actually increases accuracy slightly vs. an exhaustive search, because the classifier can incorporate information that isn't available to the PCFG. Note that this is a simple, but very effective form of coarse-to-fine pruning, with a single coarse stage (as compared to the 7-stage CTF approach in the Berkeley Parser).
To my knowledge, the Stanford Parser doesn't currently implement this pruning technique; I suspect you'd find it quite effective.
Shameless plug
The BUBS Parser implements this approach, as well as a few other optimizations, and thus achieves throughput of around 2500-5000 words per second, usually with accuracy at least equal to that I've measured with the Stanford Parser. Obviously, if you're using the rest of the Stanford pipeline, the built-in parser is already well integrated and convenient. But if you need improved speed, BUBS might be worth a look, and it does include some example code to aid in embedding the engine in a larger system.
Memoizing Common Substrings
Regarding your thoughts on pre-analyzing known noun phrases or other frequently-observed sequences with consistent structure: I did some evaluation of a similar idea a few years ago (in the context of sharing common substructures across a large corpus, when parsing on a massively parallel architecture). The preliminary results weren't encouraging.In the corpora we looked at, there just weren't enough repeated substrings of substantial length to make it worthwhile. And the aforementioned cell closure methods usually make those substrings really cheap to parse anyway.
However, if your target domains involved a lot of repetition, you might come to a different conclusion (maybe it would be effective on legal documents with lots of copy-and-paste boilerplate? Or news stories that are repeated from various sources or re-published with edits?)
I am wondered why there is no generalized parser combinators for Bottom-up parsing in Haskell like a Parsec combinators for top down parsing.
( I could find some research work went during 2004 but nothing after
https://haskell-functional-parsing.googlecode.com/files/Ljunglof-2002a.pdf
http://www.di.ubi.pt/~jpf/Site/Publications_files/technicalReport.pdf )
Is there any specific reason for not achieving it?
This is because of referential transparency. Just as no function can tell the difference between
let x = 1:x
let x = 1:1:1:x
let x = 1:1:1:1:1:1:1:1:1:... -- if this were writeable
no function can tell the difference between a grammar which is a finite graph and a grammar which is an infinite tree. Bottom-up parsing algorithms need to be able to see the grammar as a graph, in order to enumerate all the possible parsing states.
The fact that top-down parsers see their input as infinite trees allows them to be more powerful, since the tree could be computationally more complex than any graph could be; for example,
numSequence n = string (show n) *> option () (numSequence (n+1))
accepts any finite ascending sequence of numbers starting at n. This has infinitely many different parsing states. (It might be possible to represent this in a context-free way, but it would be tricky and require more understanding of the code than a parsing library is capable of, I think)
A bottom up combinator library could be written, though it is a bit ugly, by requiring all parsers to be "labelled" in such a way that
the same label always refers to the same parser, and
there is only a finite set of labels
at which point it begins to look a lot more like a traditional specification of a grammar than a combinatory specification. However, it could still be nice; you would only have to label recursive productions, which would rule out any infinitely-large rules such as numSequence.
As luqui's answer indicates a bottom-up parser combinator library is not a realistic. On the chance that someone gets to this page just looking for haskell's bottom up parser generator, what you are looking for is called the Happy parser generator. It is like yacc for haskell.
As luqui said above: Haskell's treatment of recursive parser definitions does not permit the definition of bottom-up parsing libraries. Bottom-up parsing libraries are possible though if you represent recursive grammars differently. With apologies for the self-promotion, one (research) parser library that uses such an approach is grammar-combinators. It implements a grammar transformation called the uniform Paull transformation that can be combined with the top-down parser algorithm to obtain a bottom-up parser for the original grammar.
#luqui essentially says, that there are cases in which sharing is unobservable. However, it's not the case in general: many approaches to observable sharing exist. E.g. http://www.ittc.ku.edu/~andygill/papers/reifyGraph.pdf mentions a few different methods to achieve observable sharing and proposes its own new method:
This looping structure can be used for interpretation, but not for
further analysis, pretty printing, or general processing. The
challenge here, and the subject of this paper, is how to allow trees
extracted from Haskell hosted deep DSLs to have observable back-edges,
or more generally, observable sharing. This a well-understood problem,
with a number of standard solutions.
Note that the "ugly" solution of #liqui is mentioned by the paper under the name of explicit labels. The solution proposed by the paper is still "ugly" as it uses so called "stable names", but other solutions such as http://www.cs.utexas.edu/~wcook/Drafts/2012/graphs.pdf (which relies on PHOAS) may work.
I'm reading my notes for my comparative languages class and I'm a bit confused...
What is the difference between a context-free grammar and a deterministic context-free grammar? I'm specifically reading about how parsers are O(n^3) for CFGs and compilers are O(n) for DCFGs, and don't really understand how the difference in time complexities could be that great (not to mention I'm still confused about what the characteristics that make a CFG a DCFG).
Thank you so much in advance!
Conceptually they are quite simple to understand. The context free grammars are those which can be expressed in BNF. The DCFGs are the subset for which a workable parser can be written.
In writing compilers we are only interested in DCFGs. The reason is that 'deterministic' means roughly that the next rule to be applied at any point in the parse is determined by the input so far and a finite amount of lookahead. Knuth invented the LR() compiler back in the 1960s and proved it could handle any DCFG. Since then some refinements, especially LALR(1) and LL(1), have defined grammars that can be parsed in limited memory, and techniques by which we can write them.
We also have techniques to derive parsers automatically from the BNF, if we know it's one of these grammars. Yacc, Bison and ANTLR are familiar examples.
I've never seen a parser for a NDCFG, but at any point in the parse it would potentially need to consider the whole of the input string and every possible parse that could be applied. It's not hard to see why that would get rather large and slow.
I should point out that many real languages are imperfect, in that they are not entirely context free, not unambiguous or otherwise depart from the ideal DCFG. C/C++ is a good example, but there are many others. These languages are usually handled by special purpose rules such as semantic or syntactic predicates, special case backtracking or other 'tricks' with no effect on performance.
The comments point out that certain kinds of NDCFG are common and many tools provide a way to handle them. One common problem is ambiguity. It is relatively easy to parse an ambiguous grammar by introducing a simple local semantic rule, but of course this can only ever generate one of the possible parse trees. A generalised parser for NDCFG would potentially produce all parse trees, and could perhaps allow those trees to be filtered on some arbitrary condition. I don't know any of those.
Left recursion is not a feature of NDCFG. It presents a particular challenge to the design of LL() parsers but no problems for LR() parsers.