我曾使用pyparsing进行有限词汇命令解析，但这里有一个在pyparsing基础上构建的小框架，可以解决您发布的示例问题：
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]%s\b" % (c1.upper(), c1.lower(), s[1:]))
e1.expr.exprs.append(e2)
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)
else:
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)
else:
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)
else:
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("dog")
makeNoun("girl")
makeNoun("car")
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
try:
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")
输出：
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']
随着词汇量的扩大，这很可能会变得缓慢。50万个词条？我认为一个合理的功能性词汇量大约是5-6千个单词。而且你将受到限制，只能处理有限的句子结构——自然语言正是NLTK的用途。
