parsing – Efficient Context-Free Grammar parser, preferably Python-friendly

parsing – Efficient Context-Free Grammar parser, preferably Python-friendly

By all means take a look at Pyparsing. Its the most pythonic implementations of parsing Ive come across, and its a great design from a purely academic standpoint.

I used both ANTLR and JavaCC to teach translator and compiler theory at a local university. Theyre both good and mature, but I wouldnt use them in a Python project.

That said, unlike programming languages, natural languages are much more about the semantics than about the syntax, so you could be much better off skipping the learning curves of existing parsing tools, going with a home-brewed (top-down, backtracking, unlimited lookahead) lexical analyzer and parser, and spending the bulk of your time writing the code that figures out what a parsed, but ambiguous, natural-language sentence means.

Ive used pyparsing for limited vocabulary command parsing, but here is a little framework on top of pyparsing that addresses your posted example:

from pyparsing import *

transVerb, transVerbPlural, transVerbPast, transVerbProg = (Forward() for i in range(4))
intransVerb, intransVerbPlural, intransVerbPast, intransVerbProg = (Forward() for i in range(4))
singNoun,pluralNoun,properNoun = (Forward() for i in range(3))
singArticle,pluralArticle = (Forward() for i in range(2))
verbProg = transVerbProg | intransVerbProg
verbPlural = transVerbPlural | intransVerbPlural

for expr in (transVerb, transVerbPlural, transVerbPast, transVerbProg,
            intransVerb, intransVerbPlural, intransVerbPast, intransVerbProg,
            singNoun, pluralNoun, properNoun, singArticle, pluralArticle):
    expr << MatchFirst([])

def appendExpr(e1, s):
    c1 = s[0]
    e2 = Regex(r[%s%s]%sb % (c1.upper(), c1.lower(), s[1:]))

def makeVerb(s, transitive):
    v_pl, v_sg, v_past, v_prog = s.split()
    if transitive:
        appendExpr(transVerb, v_sg)
        appendExpr(transVerbPlural, v_pl)
        appendExpr(transVerbPast, v_past)
        appendExpr(transVerbProg, v_prog)
        appendExpr(intransVerb, v_sg)
        appendExpr(intransVerbPlural, v_pl)
        appendExpr(intransVerbPast, v_past)
        appendExpr(intransVerbProg, v_prog)

def makeNoun(s, proper=False):
    if proper:
        appendExpr(properNoun, s)
        n_sg,n_pl = (s.split() + [s+s])[:2]
        appendExpr(singNoun, n_sg)
        appendExpr(pluralNoun, n_pl)

def makeArticle(s, plural=False):
    for ss in s.split():
        if not plural:
            appendExpr(singArticle, ss)
            appendExpr(pluralArticle, ss)

makeVerb(disappear disappears disappeared disappearing, transitive=False)
makeVerb(walk walks walked walking, transitive=False)
makeVerb(see sees saw seeing, transitive=True)
makeVerb(like likes liked liking, transitive=True)

makeNoun(child children)
makeNoun(Kim, proper=True)
makeNoun(Jody, proper=True)

makeArticle(a the)
makeArticle(this every)
makeArticle(the these all some several, plural=True)

transObject = (singArticle + singNoun | properNoun | Optional(pluralArticle) + pluralNoun | verbProg | to + verbPlural)
sgSentence = (singArticle + singNoun | properNoun) + (intransVerb | intransVerbPast | (transVerb | transVerbPast) + transObject)
plSentence = (Optional(pluralArticle) + pluralNoun) + (intransVerbPlural | intransVerbPast | (transVerbPlural |transVerbPast) + transObject)

sentence = sgSentence | plSentence

def test(s):
    print s
        print sentence.parseString(s).asList()
    except ParseException, pe:
        print pe

test(Kim likes cars)
test(The girl saw the dog)
test(The dog saw Jody)
test(Kim likes walking)
test(Every girl likes dogs)
test(All dogs like children)
test(Jody likes to walk)
test(Dogs like walking)
test(All dogs like walking)
test(Every child likes Jody)


Kim likes cars
[Kim, likes, cars]
The girl saw the dog
[The, girl, saw, the, dog]
The dog saw Jody
[The, dog, saw, Jody]
Kim likes walking
[Kim, likes, walking]
Every girl likes dogs
[Every, girl, likes, dogs]
All dogs like children
[All, dogs, like, children]
Jody likes to walk
[Jody, likes, to, walk]
Dogs like walking
[Dogs, like, walking]
All dogs like walking
[All, dogs, like, walking]
Every child likes Jody
[Every, child, likes, Jody]

This is likely to get slow as you expand the vocabulary. Half a million entries? I thought that a reasonable functional vocabulary was on the order of 5-6 thousand words. And you will be pretty limited in the sentence structures that you can handle – natural language is what NLTK is for.

parsing – Efficient Context-Free Grammar parser, preferably Python-friendly

Tooling aside…

You may remember from theory that there are infinite grammars that define the same language. There are criteria for categorizing grammars and determining which is the canonical or minimal one for a given language, but in the end, the best grammar is the one thats more convenient for the task and tools at hand (remember the transformations of CFGs into LL and LR grammars?).

Then, you probably dont need a huge lexicon to parse an sentence in English. Theres a lot to be known about a word in languages like German or Latin (or even Spanish), but not in the many times arbitrary and ambiguos English. You should be able to get away with a small lexicon that contains only the key words necessary to arrive to the structure of a sentence. At any rate, the grammar you choose, no matter its size, can be cached in a way that thee tooling can directly use it (i.e., you can skip parsing the grammar).

Given that, it could be a good idea to take a look at a simpler parser already worked on by someone else. There must be thousands of those in the literature. Studying different approaches will let you evaluate your own, and may lead you to adopt someone elses.

Finally, as I already mentioned, interpreting natural languages is much more about artificial intelligence than about parsing. Because structure determines meaning and meaning determines structure you have to play with both at the same time. An approach Ive seen in the literature since the 80s is to let different specialized agents take shots at solving the problem against a blackboard. With that approach syntatic and semantic analysis happen concurrently.

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