def main(arguments):
if not (arguments["--corenlp-json-input"]):
#Initialize Berekeley parser when using raw input
load_berkeley(not arguments["--tokenized"])
outputType = 'html'
sep = "<br>"
if arguments['-t']:
outputType = 'pdf'
sep = "\n"
graphical = (outputType == 'html')
# Parse according to source input
if arguments["--corenlp-json-input"]:
# Parse accroding to input method
sents = (json.loads("".join(arguments["FILE"])) \
if isinstance(arguments["FILE"], list) \
else json.load(arguments["FILE"]))["sentences"]
elif arguments["--dontfilter"]:
sents = [x for x in arguments["FILE"]]
else:
sents = [[x for x in s if x in string.printable]
for s in arguments["FILE"]]
for sent in sents:
gs = parseSentences(
sent,
HOME_DIR,
stanford_json_sent=arguments["--corenlp-json-input"])
g, tree = gs[0]
dot = g.drawToFile("", "svg")
# deptree to svg file
d = DepTreeVisualizer.from_conll_str(tree)
# print sentence (only if in graphical mode)
if (arguments["--original"]):
print((sent + sep))
#print dependency tree
if (arguments['--dep']):
if graphical:
print((d.as_svg(compact=True, flat=True) + sep))
else:
print(tree)
#print PropS output
if graphical:
print((dot.create(format='svg') + sep))
else:
print(g)
#print open ie like extractions
if (arguments["--oie"]):
print((sep.join(
[str(prop) for prop in g.getPropositions(outputType)])))
def main(arguments):
if not(arguments["--corenlp-json-input"]):
#Initialize Berekeley parser when using raw input
load_berkeley(not arguments["--tokenized"])
outputType = 'html'
sep = "<br>"
if arguments['-t']:
outputType = 'pdf'
sep = "\n"
graphical = (outputType=='html')
# Parse according to source input
if arguments["--corenlp-json-input"]:
# Parse accroding to input method
sents = (json.loads("".join(arguments["FILE"])) \
if isinstance(arguments["FILE"], list) \
else json.load(arguments["FILE"]))["sentences"]
elif arguments["--dontfilter"]:
sents = [x for x in arguments["FILE"]]
else:
sents = [filter(lambda x: x in string.printable, s) for s in arguments["FILE"]]
for sent in sents:
gs = parseSentences(sent,
HOME_DIR,
stanford_json_sent = arguments["--corenlp-json-input"])
g,tree = gs[0]
dot = g.drawToFile("","svg")
# deptree to svg file
d = DepTreeVisualizer.from_conll_str(tree)
# print sentence (only if in graphical mode)
if (arguments["--original"]):
print(sent+sep)
#print dependency tree
if (arguments['--dep']):
if graphical:
print(d.as_svg(compact=True,flat=True)+sep)
else:
print(tree)
#print PropS output
if graphical:
print(dot.create(format='svg')+sep)
else:
print(g)
#print open ie like extractions
if (arguments["--oie"]):
print(sep.join([str(prop) for prop in g.getPropositions(outputType)]))
def main(arguments):
load_berkeley(not arguments["--tokenized"])
outputType = 'html'
sep = "<br>"
if arguments['-t']:
outputType = 'pdf'
sep = "\n"
graphical = (outputType == 'html')
if arguments["--dontfilter"]:
sents = [x for x in arguments["FILE"]]
else:
sents = [
filter(lambda x: x in string.printable, s)
for s in arguments["FILE"]
]
for sent in sents:
gs = parseSentences(sent, HOME_DIR)
g, tree = gs[0]
dot = g.drawToFile("", "svg")
# deptree to svg file
d = DepTreeVisualizer.from_conll_str(tree)
# print sentence (only if in graphical mode)
if (arguments["--original"]):
print(sent + sep)
#print dependency tree
if (arguments['--dep']):
if graphical:
print(d.as_svg(compact=True, flat=True) + sep)
else:
print(tree)
#print PropS output
if graphical:
print(dot.create(format='svg') + sep)
else:
print(g)
#print open ie like extractions
if (arguments["--oie"]):
print(
sep.join([str(prop)
for prop in g.getPropositions(outputType)]))
def gparse():
print "in gparse"
sent = request.GET.get('text','').strip()
b = BratVisualizer()
print sent
sents = sent.strip().replace(". ",".\n").replace("? ","?\n").replace("! ","!\n").split("\n")
sent = sents[0]
gs = parseSentences(sent)
g,tree = gs[0]
ret = b.to_html(g)
ret = ret.replace('PROPOSITIONS_STUB', '<br>'.join([str(prop) for prop in g.getPropositions('html')]))
print "returning...."
return ret
def gparse():
print "in gparse"
sent = request.GET.get('text','').strip()
b = BratVisualizer()
print sent
sents = sent.strip().replace(". ",".\n").replace("? ","?\n").replace("! ","!\n").split("\n")
sent = sents[0]
gs = parseSentences(sent)
g,tree = gs[0]
ret = b.to_html(g)
ret = ret.replace('PROPOSITIONS_STUB', '<br>'.join([str(prop) for prop in g.getPropositions('html')]))
print "returning...."
return ret
def main(arguments):
load_berkeley(not arguments["--tokenized"])
outputType = 'html'
sep = "<br>"
if arguments['-t']:
outputType = 'pdf'
sep = "\n"
graphical = (outputType=='html')
if arguments["--dontfilter"]:
sents = [x for x in arguments["FILE"]]
else:
sents = [filter(lambda x: x in string.printable, s) for s in arguments["FILE"]]
for sent in sents:
gs = parseSentences(sent,HOME_DIR)
g,tree = gs[0]
dot = g.drawToFile("","svg")
# deptree to svg file
d = DepTreeVisualizer.from_conll_str(tree)
# print sentence (only if in graphical mode)
if (arguments["--original"]):
print(sent+sep)
#print dependency tree
if (arguments['--dep']):
if graphical:
print(d.as_svg(compact=True,flat=True)+sep)
else:
print(tree)
#print PropS output
if graphical:
print(dot.create(format='svg')+sep)
else:
print(g)
#print open ie like extractions
if (arguments["--oie"]):
print(sep.join([str(prop) for prop in g.getPropositions(outputType)]))
def single_sentence_props(sent):
"""
Return a graph representation of a single sentence with PropS
"""
g, tree = parseSentences(sent, props_path)[0]
return g
def getProps(self, sent):
''' returns the textual props representation of an input sentence '''
g, tree = parseSentences(sent)[0]
return str(g)
def main(arguments):
load_berkeley(not arguments["--tokenized"])
outputType = 'html'
sep = "<br>"
if True or arguments['-t']:
outputType = 'pdf'
sep = "\n"
graphical = (outputType == 'html')
if arguments["--dontfilter"]:
sents = [x for x in arguments["FILE"]]
else:
sents = [
filter(lambda x: x in string.printable, s)
for s in arguments["FILE"]
]
numSent = 0
for sent in sents:
# be kind to the downstream chain -- do not send blank lines!
if sent.strip() == '':
continue
numSent = numSent + 1
# print sentence (only if in graphical mode)
if (arguments["--original"]):
print(sent)
gs = parseSentences(sent, HOME_DIR)
g, tree = gs[0]
dot = g.drawToFile("", "svg")
# deptree to svg file
d = DepTreeVisualizer.from_conll_str(tree)
#print dependency tree
if (arguments['--dep']):
if graphical:
print(d.as_svg(compact=True, flat=True) + sep)
else:
print(tree)
if (arguments['-t']):
print(dot.create(format='svg') + sep if graphical else g)
if arguments['-l'] or arguments['-c']:
lf = g.toLogicForm() # to_de_bruijn(g.toLogicForm())
if arguments['-l']:
pprint(lf)
if arguments['-c']:
lf = lf_clean(lf)
pprint(lf)
#print open ie like extractions
if (arguments["--oie"]):
print(
sep.join([str(prop)
for prop in g.getPropositions(outputType)]))
#end for loop
print('Processed {0} sentences.'.format(numSent))
def main(arguments):
if not(arguments["--corenlp-json-input"]):
#Initialize Berekeley parser when using raw input
load_berkeley(not arguments["--tokenized"])
outputType = 'html'
sep = "<br>"
if arguments['-t']:
outputType = 'pdf'
sep = "\n"
graphical = (outputType=='html')
# Parse according to source input
if arguments["--corenlp-json-input"]:
# Parse accroding to input method
sents = (json.loads("".join(arguments["FILE"])) \
if isinstance(arguments["FILE"], list) \
else json.load(arguments["FILE"]))["sentences"]
elif arguments["--dontfilter"]:
sents = [x for x in arguments["FILE"]]
else:
sents = [filter(lambda x: x in string.printable, s) for s in arguments["FILE"]]
numSent = 0
for sent in sents:
# be kind to the downstream chain -- do not send blank lines!
if isinstance(sent, basestring) and sent.strip() == '':
continue
numSent=numSent+1
# print sentence (only if in graphical mode)
if (arguments["--original"]):
print(sent)
gs = parseSentences(sent,
HOME_DIR,
stanford_json_sent = arguments["--corenlp-json-input"])
g,tree = gs[0]
dot = g.drawToFile("","svg")
# deptree to svg file
d = DepTreeVisualizer.from_conll_str(tree)
#print dependency tree
if (arguments['--dep']):
if graphical:
print(d.as_svg(compact=True,flat=True)+sep)
else:
print(tree)
if (arguments['-t']):
print(dot.create(format='svg')+sep if graphical else g)
if arguments['-l'] or arguments['-c']:
lf = g.toLogicForm() # to_de_bruijn(g.toLogicForm())
if arguments['-l']:
pprint(lf)
if arguments['-c']:
lf = lf_clean(lf)
pprint(lf)
#print open ie like extractions
if (arguments["--oie"]):
print(sep.join([str(prop) for prop in g.getPropositions(outputType)]))
#end for loop
print('Processed {0} sentences.'.format(numSent))