def demo():
"""
A demonstration that shows the output of several different
tokenizers on the same string.
"""
from en.parser.nltk_lite import tokenize
# Define the test string.
s = "Good muffins cost $3.88\nin New York. Please buy me\ntwo of them.\n\nThanks."
print 'Input text:'
print `s`
print
print 'Tokenize using whitespace:'
_display(tokenize.whitespace(s))
print
print 'Tokenize sequences of alphanumeric characters:'
_display(tokenize.regexp(s, pattern=r'\w+', gaps=False))
print
print 'Tokenize sequences of letters and sequences of nonletters:'
_display(tokenize.wordpunct(s))
print
print 'Tokenize by lines:'
_display(tokenize.line(s))
print
print 'Tokenize by blank lines:'
_display(tokenize.blankline(s))
print
print 'A simple sentence tokenizer:'
_display(tokenize.regexp(s, pattern=r'\.(\s+|$)', gaps=True))
print
def demo():
"""
A demonstration that shows the output of several different
tokenizers on the same string.
"""
from en.parser.nltk_lite import tokenize
# Define the test string.
s = "Good muffins cost $3.88\nin New York. Please buy me\ntwo of them.\n\nThanks."
print 'Input text:'
print ` s `
print
print 'Tokenize using whitespace:'
_display(tokenize.whitespace(s))
print
print 'Tokenize sequences of alphanumeric characters:'
_display(tokenize.regexp(s, pattern=r'\w+', gaps=False))
print
print 'Tokenize sequences of letters and sequences of nonletters:'
_display(tokenize.wordpunct(s))
print
print 'Tokenize by lines:'
_display(tokenize.line(s))
print
print 'Tokenize by blank lines:'
_display(tokenize.blankline(s))
print
print 'A simple sentence tokenizer:'
_display(tokenize.regexp(s, pattern=r'\.(\s+|$)', gaps=True))
print
def conll_chunk(s, chunk_types=("NP",), top_node="S"):
"""
@return: A chunk structure for a single sentence
encoded in the given CONLL 2000 style string.
@rtype: L{Tree}
"""
stack = [Tree(top_node, [])]
for lineno, line in enumerate(tokenize.line(s)):
# Decode the line.
match = _LINE_RE.match(line)
if match is None:
raise ValueError, 'Error on line %d' % lineno
(word, tag, state, chunk_type) = match.groups()
# If it's a chunk type we don't care about, treat it as O.
if (chunk_types is not None and
chunk_type not in chunk_types):
state = 'O'
# For "Begin"/"Outside", finish any completed chunks -
# also do so for "Inside" which don't match the previous token.
mismatch_I = state == 'I' and chunk_type != stack[-1].node
if state in 'BO' or mismatch_I:
if len(stack) == 2: stack.pop()
# For "Begin", start a new chunk.
if state == 'B' or mismatch_I:
chunk = Tree(chunk_type, [])
stack[-1].append(chunk)
stack.append(chunk)
# Add the new word token.
stack[-1].append((word, tag))
return stack[0]
def conll_chunk(s, chunk_types=("NP", ), top_node="S"):
"""
@return: A chunk structure for a single sentence
encoded in the given CONLL 2000 style string.
@rtype: L{Tree}
"""
stack = [Tree(top_node, [])]
for lineno, line in enumerate(tokenize.line(s)):
# Decode the line.
match = _LINE_RE.match(line)
if match is None:
raise ValueError('Error on line %d' % lineno)
(word, tag, state, chunk_type) = match.groups()
# If it's a chunk type we don't care about, treat it as O.
if (chunk_types is not None and chunk_type not in chunk_types):
state = 'O'
# For "Begin"/"Outside", finish any completed chunks -
# also do so for "Inside" which don't match the previous token.
mismatch_I = state == 'I' and chunk_type != stack[-1].node
if state in 'BO' or mismatch_I:
if len(stack) == 2: stack.pop()
# For "Begin", start a new chunk.
if state == 'B' or mismatch_I:
chunk = Tree(chunk_type, [])
stack[-1].append(chunk)
stack.append(chunk)
# Add the new word token.
stack[-1].append((word, tag))
return stack[0]
def _list_sent(sent):
return [tokenize.whitespace(line) for line in tokenize.line(sent)]
def _list_sent(sent):
return [tokenize.whitespace(line) for line in tokenize.line(sent)]
