def __init__(self, **property_names):
PropertyIndirectionMixIn.__init__(self, **property_names)
# A token reader for processing sentences.
self._sent_reader = ChunkedTaggedTokenReader(top_node='S',
chunk_node='NP',
**property_names)
class TreebankTaggedTokenReader(TokenReaderI, PropertyIndirectionMixIn):
"""
A token reader that reas the treebank tagged-file format into a
token. In this format:
- Paragraphs are separated by lines of C{'='} characters.
- Sentences are separated by words tagged as sentence-final
punctuation (e.g., C{'./.'}).
- NP chunk structure is encoded with square brackets (C{[...]}).
- Words are separated by whitespace or square brackets.
- Each word has the form C{I{text}/i{tag}}, where C{I{text}}
is the word's text, and C{I{tag}} is its tag.
In the returned token:
- The returned token describes a single document.
- The document's C{SENTS} property contains a list of
sentence tokens.
- Each sentence token's C{WORDS} property contains a list of
word tokens.
- Each word token's C{TEXT} property contains the word's
text.
- Each word token's C{TAG} property contains the word's
tag.
- Depending on the arguments to the reader's constructor,
each word token may also define the C{LOC} and
C{CONTEXT} properties.
- Each sentence token's C{TREE} property contains the
chunk structures in the text. In the case of the Treebank,
these chunk structures were generated by a stochastic NP
chunker as part of the PARTS preprocessor, and \"are best
ignored.\"
"""
def __init__(self, **property_names):
PropertyIndirectionMixIn.__init__(self, **property_names)
# A token reader for processing sentences.
self._sent_reader = ChunkedTaggedTokenReader(top_node='S',
chunk_node='NP',
**property_names)
def read_token(self, s, add_contexts=False, add_locs=False, source=None):
assert chktype(1, s, str)
TEXT = self.property('TEXT')
LOC = self.property('LOC')
CONTEXT = self.property('CONTEXT')
SENTS = self.property('SENTS')
TREE = self.property('TREE')
sentences = re.findall('(?s)\S.*?/\.', s)
sent_toks = []
for sent_num, sentence in enumerate(sentences):
sent_loc = SentIndexLocation(sent_num, source)
sent_tok = self._sent_reader.read_token(sentence,
add_contexts=add_contexts,
add_locs=add_locs,
source=sent_loc)
sent_toks.append(sent_tok)
tok = Token(**{SENTS: sent_toks})
# Add context pointers, if requested
if add_contexts:
for i, sent_tok in enumerate(tok[SENTS]):
sent_tok[CONTEXT] = SubtokenContextPointer(tok, SENTS, i)
# Return the finished token.
return tok
def read_tokens(self, s, source=None):
return [self.read_token(s, source)]
class TreebankTaggedTokenReader(TokenReaderI, PropertyIndirectionMixIn):
"""
A token reader that reas the treebank tagged-file format into a
token. In this format:
- Paragraphs are separated by lines of C{'='} characters.
- Sentences are separated by words tagged as sentence-final
punctuation (e.g., C{'./.'}).
- NP chunk structure is encoded with square brackets (C{[...]}).
- Words are separated by whitespace or square brackets.
- Each word has the form C{I{text}/i{tag}}, where C{I{text}}
is the word's text, and C{I{tag}} is its tag.
In the returned token:
- The returned token describes a single document.
- The document's C{SENTS} property contains a list of
sentence tokens.
- Each sentence token's C{WORDS} property contains a list of
word tokens.
- Each word token's C{TEXT} property contains the word's
text.
- Each word token's C{TAG} property contains the word's
tag.
- Depending on the arguments to the reader's constructor,
each word token may also define the C{LOC} and
C{CONTEXT} properties.
- Each sentence token's C{TREE} property contains the
chunk structures in the text. In the case of the Treebank,
these chunk structures were generated by a stochastic NP
chunker as part of the PARTS preprocessor, and \"are best
ignored.\"
"""
def __init__(self, **property_names):
PropertyIndirectionMixIn.__init__(self, **property_names)
# A token reader for processing sentences.
self._sent_reader = ChunkedTaggedTokenReader(
top_node='S', chunk_node='NP', **property_names)
def read_token(self, s, add_contexts=False, add_locs=False,
source=None):
assert chktype(1, s, str)
TEXT = self.property('TEXT')
LOC = self.property('LOC')
CONTEXT = self.property('CONTEXT')
SENTS = self.property('SENTS')
TREE = self.property('TREE')
sentences = re.findall('(?s)\S.*?/\.', s)
sent_toks = []
for sent_num, sentence in enumerate(sentences):
sent_loc = SentIndexLocation(sent_num, source)
sent_tok = self._sent_reader.read_token(
sentence, add_contexts=add_contexts,
add_locs=add_locs, source=sent_loc)
sent_toks.append(sent_tok)
tok = Token(**{SENTS: sent_toks})
# Add context pointers, if requested
if add_contexts:
for i, sent_tok in enumerate(tok[SENTS]):
sent_tok[CONTEXT] = SubtokenContextPointer(tok, SENTS, i)
# Return the finished token.
return tok
def read_tokens(self, s, source=None):
return [self.read_token(s, source)]
def __init__(self, **property_names):
PropertyIndirectionMixIn.__init__(self, **property_names)
# A token reader for processing sentences.
self._sent_reader = ChunkedTaggedTokenReader(
top_node='S', chunk_node='NP', **property_names)
from nltk.tokenreader.tagged import ChunkedTaggedTokenReader chunked_string = "[ the/DT little/JJ cat/NN ] sat/VBD on/IN [ the/DT mat/NN ]" reader = ChunkedTaggedTokenReader(chunk_node='NP', SUBTOKENS='WORDS') sent_token = reader.read_token(chunked_string) print sent_token['TREE']