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))