def batch_parse(inputs, grammar, trace=0):
"""
Convert input sentences into syntactic trees.
:param inputs: sentences to be parsed
:type inputs: list of str
:param grammar: L{FeatureGrammar} or name of feature-based grammar
:rtype: dict
:return: a mapping from input sentences to a list of L{Tree}s
"""
# put imports here to avoid circult dependencies
from nltk.grammar import FeatureGrammar
from nltk.parse import FeatureChartParser, load_parser
if isinstance(grammar, FeatureGrammar):
cp = FeatureChartParser(grammar)
else:
cp = load_parser(grammar, trace=trace)
parses = []
for sent in inputs:
tokens = sent.split() # use a tokenizer?
syntrees = cp.nbest_parse(tokens)
parses.append(syntrees)
return parses
def batch_parse(inputs, grammar, trace=0):
"""
Convert input sentences into syntactic trees.
:param inputs: sentences to be parsed
:type inputs: list of str
:param grammar: ``FeatureGrammar`` or name of feature-based grammar
:rtype: dict
:return: a mapping from input sentences to a list of ``Tree``s
"""
# put imports here to avoid circult dependencies
from nltk.grammar import FeatureGrammar
from nltk.parse import FeatureChartParser, load_parser
if isinstance(grammar, FeatureGrammar):
cp = FeatureChartParser(grammar)
else:
cp = load_parser(grammar, trace=trace)
parses = []
for sent in inputs:
tokens = sent.split() # use a tokenizer?
syntrees = cp.nbest_parse(tokens)
parses.append(syntrees)
return parses
def parse_sents(inputs, grammar, trace=0):
"""
Convert input sentences into syntactic trees.
:param inputs: sentences to be parsed
:type inputs: list(str)
:param grammar: ``FeatureGrammar`` or name of feature-based grammar
:type grammar: nltk.grammar.FeatureGrammar
:rtype: list(nltk.tree.Tree) or dict(list(str)): list(Tree)
:return: a mapping from input sentences to a list of ``Tree``s
"""
# put imports here to avoid circult dependencies
from nltk.grammar import FeatureGrammar
from nltk.parse import FeatureChartParser, load_parser
if isinstance(grammar, FeatureGrammar):
cp = FeatureChartParser(grammar)
else:
cp = load_parser(grammar, trace=trace)
parses = []
for sent in inputs:
tokens = sent.split() # use a tokenizer?
syntrees = list(cp.parse(tokens))
parses.append(syntrees)
return parses
def parse_sents(inputs, grammar, trace=0):
"""
Convert input sentences into syntactic trees.
:param inputs: sentences to be parsed
:type inputs: list(str)
:param grammar: ``FeatureGrammar`` or name of feature-based grammar
:type grammar: nltk.grammar.FeatureGrammar
:rtype: list(nltk.tree.Tree) or dict(list(str)): list(Tree)
:return: a mapping from input sentences to a list of ``Tree`` instances.
"""
# put imports here to avoid circult dependencies
from nltk.grammar import FeatureGrammar
from nltk.parse import FeatureChartParser, load_parser
if isinstance(grammar, FeatureGrammar):
cp = FeatureChartParser(grammar)
else:
cp = load_parser(grammar, trace=trace)
parses = []
for sent in inputs:
tokens = sent.split() # use a tokenizer?
syntrees = list(cp.parse(tokens))
parses.append(syntrees)
return parses
use_local_file = False
if use_local_file:
if 'hw6' in os.listdir():
os.chdir('hw6')
input_pcfg_filename = 'grammar.fcfg'
sentences_filename = 'sentences.txt'
output_parses_filename = "hw6_output.txt"
else:
input_pcfg_filename = sys.argv[1]
sentences_filename = sys.argv[2]
output_parses_filename = sys.argv[3]
gr = nltk.data.load(input_pcfg_filename,
format='fcfg') # read fcfg grammar
parser = FeatureChartParser(gr) # Initialize parserxx
output_file = open(output_parses_filename, 'w')
# Parse sentences
with open(sentences_filename, 'r') as sent_file:
line = sent_file.readline().strip('\n')
while line:
output_file.write(line + '\n')
words = nltk.word_tokenize(line)
try:
parsed = parser.parse(words)
tree = parsed.__iter__()
out_tree = ''
for tree in parsed:
out_tree = str(tree.label()['SEM'].simplify())