from bllipparser import RerankingParser, tokenize
print "start loading model..."
rrp = RerankingParser.load_unified_model_dir('/home/yukang/selftrained')
print "finish loading model"
inputfile = "wsjtest"
outputfile = "wsjtest.reparse"
count = 0
data = open(inputfile)
output = open(outputfile, 'w')
sentence = []
for line in data:
if len(line.split()) == 0:
if len(sentence) == 0:
continue
count += 1
print "start solving", count
#last line of the file must be a blank line to terminate the last sentence.
l = [
word[0].replace("(", "-LRB-").replace(")", "-RRB-")
for word in sentence
]
ans = rrp.parse(l)
output.write(str(ans[0].ptb_parse) + "\n")
# if count > 1:
# break
sentence = []
else:
parts = line.split()
sentence.append(parts)
output.close()
from bllipparser import RerankingParser, tokenize
print "start loading model..."
rrp = RerankingParser.load_unified_model_dir('/home/yukang/selftrained')
print "finish loading model"
inputfile = "wsjtest"
outputfile = "wsjtest.reparse"
count = 0
data = open(inputfile)
output = open(outputfile, 'w')
sentence = []
for line in data:
if len(line.split())==0:
if len(sentence)==0:
continue
count+=1
print "start solving", count
#last line of the file must be a blank line to terminate the last sentence.
l = [word[0].replace("(", "-LRB-").replace(")", "-RRB-") for word in sentence]
ans = rrp.parse(l)
output.write(str(ans[0].ptb_parse)+"\n")
# if count > 1:
# break
sentence = []
else:
parts = line.split()
sentence.append(parts)
output.close()
