def add_JAMR_align(instances,aligned_amr_file):
comments,amr_strings = readAMR(aligned_amr_file)
for i in range(len(instances)):
amr = AMR.parse_string(amr_strings[i])
alignment = Aligner.readJAMRAlignment(amr,comments[i]['alignments'])
ggraph = SpanGraph.init_ref_graph(amr,alignment,instances[i].tokens)
ggraph.pre_merge_netag(instances[i])
#print >> log, "Graph ID:%s\n%s\n"%(ggraph.graphID,ggraph.print_tuples())
instances[i].addAMR(amr)
instances[i].addGoldGraph(ggraph)
def add_JAMR_align(instances, aligned_amr_file):
comments, amr_strings = readAMR(aligned_amr_file)
for i in range(len(instances)):
amr = AMR.parse_string(amr_strings[i])
alignment = Aligner.readJAMRAlignment(amr, comments[i]['alignments'])
ggraph = SpanGraph.init_ref_graph(amr, alignment, instances[i].tokens)
ggraph.pre_merge_netag(instances[i])
#print >> log, "Graph ID:%s\n%s\n"%(ggraph.graphID,ggraph.print_tuples())
instances[i].addAMR(amr)
instances[i].addGoldGraph(ggraph)
def _init_instances(sent_file, amr_strings, comments):
print >> log, "Preprocess 1:pos, ner and dependency using stanford parser..."
proc = StanfordCoreNLP()
instances = proc.parse(sent_file)
print >> log, "Preprocess 2:adding amr and generating gold graph"
assert len(instances) == len(amr_strings)
for i in range(len(instances)):
amr = AMR.parse_string(amr_strings[i])
instances[i].addAMR(amr)
alignment = Aligner.readJAMRAlignment(amr, comments[i]['alignments'])
ggraph = SpanGraph.init_ref_graph(amr, alignment, comments[i]['snt'])
ggraph.pre_merge_netag(instances[i])
instances[i].addGoldGraph(ggraph)
return instances
def _init_instances(sent_file,amr_strings,comments):
print >> log, "Preprocess 1:pos, ner and dependency using stanford parser..."
proc = StanfordCoreNLP()
instances = proc.parse(sent_file)
print >> log, "Preprocess 2:adding amr and generating gold graph"
assert len(instances) == len(amr_strings)
for i in range(len(instances)):
amr = AMR.parse_string(amr_strings[i])
instances[i].addAMR(amr)
alignment = Aligner.readJAMRAlignment(amr,comments[i]['alignments'])
ggraph = SpanGraph.init_ref_graph(amr,alignment,comments[i]['snt'])
ggraph.pre_merge_netag(instances[i])
instances[i].addGoldGraph(ggraph)
return instances
def preprocess(amr_file,START_SNLP=True):
'''nasty function'''
aligned_amr_file = amr_file + '.aligned'
if os.path.exists(aligned_amr_file):
comments,amr_strings = readAMR(aligned_amr_file)
else:
comments,amr_strings = readAMR(amr_file)
#comments,amr_strings = readAMR(aligned_amr_file)
sentences = [c['snt'] for c in comments]
tmp_sentence_file = amr_file+'.sent'
if not os.path.exists(tmp_sentence_file):
_write_sentences(tmp_sentence_file,sentences)
print >> log, "pos, ner and dependency..."
proc = StanfordCoreNLP()
if START_SNLP: proc.setup()
instances = proc.parse(tmp_sentence_file)
tok_amr_filename = amr_file + '.tok'
if not os.path.exists(tok_amr_filename):
_write_tok_amr(tok_amr_filename,amr_file,instances)
SpanGraph.graphID = 0
for i in range(len(instances)):
amr = AMR.parse_string(amr_strings[i])
alignment = Aligner.readJAMRAlignment(amr,comments[i]['alignments'])
ggraph = SpanGraph.init_ref_graph(amr,alignment,instances[i].tokens)
#ggraph.pre_merge_netag(instances[i])
#print >> log, "Graph ID:%s\n%s\n"%(ggraph.graphID,ggraph.print_tuples())
instances[i].addAMR(amr)
instances[i].addGoldGraph(ggraph)
#print >> log, "adding amr"
#_add_amr(instances,amr_strings)
#if writeToFile:
# output_file = amr_file.rsplit('.',1)[0]+'_dataInst.p'
# pickle.dump(instances,open(output_file,'wb'),pickle.HIGHEST_PROTOCOL)
return instances
def preprocess(amr_file, START_SNLP=True):
'''nasty function'''
aligned_amr_file = amr_file + '.aligned'
if os.path.exists(aligned_amr_file):
comments, amr_strings = readAMR(aligned_amr_file)
else:
comments, amr_strings = readAMR(amr_file)
#comments,amr_strings = readAMR(aligned_amr_file)
sentences = [c['snt'] for c in comments]
tmp_sentence_file = amr_file + '.sent'
if not os.path.exists(tmp_sentence_file):
_write_sentences(tmp_sentence_file, sentences)
print >> log, "pos, ner and dependency..."
proc = StanfordCoreNLP()
if START_SNLP: proc.setup()
instances = proc.parse(tmp_sentence_file)
tok_amr_filename = amr_file + '.tok'
if not os.path.exists(tok_amr_filename):
_write_tok_amr(tok_amr_filename, amr_file, instances)
SpanGraph.graphID = 0
for i in range(len(instances)):
amr = AMR.parse_string(amr_strings[i])
alignment = Aligner.readJAMRAlignment(amr, comments[i]['alignments'])
ggraph = SpanGraph.init_ref_graph(amr, alignment, instances[i].tokens)
#ggraph.pre_merge_netag(instances[i])
#print >> log, "Graph ID:%s\n%s\n"%(ggraph.graphID,ggraph.print_tuples())
instances[i].addAMR(amr)
instances[i].addGoldGraph(ggraph)
#print >> log, "adding amr"
#_add_amr(instances,amr_strings)
#if writeToFile:
# output_file = amr_file.rsplit('.',1)[0]+'_dataInst.p'
# pickle.dump(instances,open(output_file,'wb'),pickle.HIGHEST_PROTOCOL)
return instances
