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depparser.py
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depparser.py
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#!/usr/bin/env python
import os,subprocess
VERBOSE = True
class DepParser(object):
def __init__(self):
pass
def parse(self,sent_filename):
'''the input should be tokenized sentences (tokenized by stanford CoreNLP) '''
raise NotImplemented("Must implement setup method!")
import sys, traceback
from multiprocessing import Queue, Process, Lock, JoinableQueue, cpu_count
from multiprocessing.sharedctypes import Value
from Queue import Empty, Full
from progress import Progress
# parse process body
def parse_queue(parser, queue, results, count, sync_after=0, p=None, i=-1):
local_count = 0
try:
while True:
item = queue.get(True, 2)
r = "(())" # dummy output if wasn't able to parse
try:
ll = item[1].strip()
if ll:
r = parser.simple_parse(ll.split()) # call parser
except KeyboardInterrupt:
raise
except Exception as e:
print >> sys.stderr, 'WARNING: unable to parse sentence:'
print >> sys.stderr, ll.strip()
results.put((item[0], r))
local_count += 1
queue.task_done()
if local_count >= sync_after:
count.value += local_count
local_count = 0
if p is not None:
p.set(count.value)
except KeyboardInterrupt:
# print >> sys.stderr, 'Job interrupted'
os.abort() # abort instead of exit so that multiprocessing won't wait
return
except Empty:
pass
if local_count > 0:
count.value += local_count
if p is not None:
p.set(count.value)
class CharniakParser(DepParser):
parser = None
def __init__(self):
if CharniakParser.parser is None:
from bllipparser.ModelFetcher import download_and_install_model
from bllipparser import RerankingParser
model_type = 'WSJ+Gigaword'
path_to_model = download_and_install_model(model_type,'./bllip-parser/models')
print "Loading Charniak parser model: %s ..." % (model_type)
CharniakParser.parser = RerankingParser.from_unified_model_dir(path_to_model)
def parse(self,sent_filename):
"""
use Charniak parser to parse sentences then convert results to Stanford Dependency
"""
rrp = CharniakParser.parser
print "Begin Charniak parsing ..."
parsed_filename = sent_filename+'.charniak.parse'
parsed_trees = ''
# will use multiprocessing to parallelize parsing
queue = JoinableQueue()
results = Queue()
print 'Reading', sent_filename, '...'
data = []
with open(sent_filename,'rb') as f:
for line in f:
l = line.decode('utf8', errors='ignore')
# queue.put((len(data), l))
data.append(l)
feed = enumerate(data)
fed_count = 0
# feed first 100 items
for item in feed:
queue.put(item)
data[item[0]] = ''
fed_count += 1
if item[0] >= 1024:
break
p = Progress(len(data), estimate=True, values=True) # output progress bar
# define jobs
count = Value('i', 0)
num_threads = cpu_count()
sync_count = len(data)/1000/num_threads
print 'Starting %i jobs ...' % num_threads
jobs = [Process(target=parse_queue, args=(rrp, queue, results, count, sync_count, p if i == -1 else None, i)) for i in range(num_threads)]
try:
# start jobs
for job in jobs:
job.start()
total_count = 0
# feed rest items
while fed_count < len(data):
for item in feed:
while True:
try:
queue.put(item, True, 0.3)
data[item[0]] = ''
fed_count += 1
break
except Full:
# gather some results
try:
while True:
i,v = results.get(True, 0.3)
data[i] = v
total_count += 1
p.set(count.value)
except Empty:
pass
p.set(count.value)
# gathering results from jobs
while total_count < len(data):
try:
i,v = results.get(True, 0.3) # timeout delay small enough to update progress bar, see below
data[i] = v
total_count += 1
except Empty:
pass
p.set(count.value) # even if no results are received (cached somewhere), the counter will be updated after get() timeout above
# NOTE: There might be a slight delay after reaching 100%, because the finished results counter is ahead of received results counter;
# will stay at 100% until all results are received.
p.set(total_count)
p.complete()
# wait for jobs to finish
queue.join()
for job in jobs:
job.join()
except KeyboardInterrupt:
print >> sys.stderr, '\nInterrupted, aborting'
os.abort() # abort instead of exit so that multiprocessing won't wait
print 'Writing', parsed_filename, '...'
with open(parsed_filename, 'w') as f:
for item in data:
print >> f, item
# convert parse tree to dependency tree
print "Convert Charniak parse tree to Stanford Dependency tree ..."
subprocess.call('./scripts/stdconvert.sh '+parsed_filename,shell=True)
class StanfordDepParser(DepParser):
def parse(self,sent_filename):
"""
separate dependency parser
"""
# jars = ["stanford-parser-3.3.1-models.jar",
# "stanford-parser.jar"]
jars = ["stanford-corenlp-3.2.0-models.jar",
"stanford-corenlp-3.2.0.jar"]
# if CoreNLP libraries are in a different directory,
# change the corenlp_path variable to point to them
stanford_path = os.path.join(os.path.dirname(__file__), "stanfordnlp/stanford-corenlp-full-2013-06-20")
java_path = "java"
classname = "edu.stanford.nlp.parser.lexparser.LexicalizedParser"
# include the properties file, so you can change defaults
# but any changes in output format will break parse_parser_results()
#props = "-props default.properties"
flags = "-tokenized -sentences newline -outputFormat typedDependencies -outputFormatOptions basicDependencies,markHeadNodes"
# add and check classpaths
model = "edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz"
jars = [os.path.join(stanford_path, jar) for jar in jars]
for jar in jars:
if not os.path.exists(jar):
print "Error! Cannot locate %s" % jar
import sys
sys.exit(1)
#Change from ':' to ';'
# spawn the server
start_depparser = "%s -Xmx2500m -cp %s %s %s %s %s" % (java_path, ':'.join(jars), classname, flags, model, sent_filename)
if VERBOSE: print start_depparser
#incoming = pexpect.run(start_depparser)
process = subprocess.Popen(start_depparser.split(),shell=False,stdout=subprocess.PIPE)
incoming = process.communicate()[0]
print 'Incoming',incoming
return incoming
class ClearDepParser(DepParser):
def parse(self,sent_filename):
subprocess.call(["cp",sent_filename,sent_filename+'.tmp'])
subprocess.call(["sed","-i",r':a;N;$!ba;s/\n/\n\n/g',sent_filename])
subprocess.call(["sed","-i",r':a;N;$!ba;s/\s/\n/g',sent_filename])
clear_path="/home/j/llc/cwang24/Tools/clearnlp"
extension = "clear.dep"
start_depparser = "%s/clearnlp-parse %s %s" % (clear_path,sent_filename,extension)
print start_depparser
extcode = subprocess.call(start_depparser,shell=True)
dep_result = open(sent_filename+'.'+extension,'r').read()
subprocess.call(["mv",sent_filename+'.tmp',sent_filename])
return dep_result
class TurboDepParser(DepParser):
def parse(self,sent_filename):
turbo_path="/home/j/llc/cwang24/Tools/TurboParser"
extension = "turbo.dep"
start_depparser = "%s/scripts/parse-tok.sh %s %s" % (turbo_path,sent_filename,sent_filename+'.'+extension)
print start_depparser
subprocess.call(start_depparser,shell=True)
dep_result = open(sent_filename+'.'+extension,'r').read()
return dep_result
class MateDepParser(DepParser):
def parse(self,sent_filename):
mate_path="/home/j/llc/cwang24/Tools/MateParser"
extension = "mate.dep"
start_depparser = "%s/parse-eng %s %s" % (mate_path,sent_filename,sent_filename+'.'+extension)
print start_depparser
subprocess.call(start_depparser,shell=True)
dep_result = open(sent_filename+'.'+extension,'r').read()
return dep_result