def parseXML(xml, intermediateFileDir, debug=False):
preprocessor = Preprocessor()
preprocessor.setArgForAllSteps("debug", debug)
preprocessor.stepArgs("PARSE")["requireEntities"] = False
#preprocessor.process(xml, intermediateFileDir, fromStep="SPLIT-SENTENCES", toStep="FIND-HEADS", omitSteps=["NER"])
#preprocessor.process(xml, intermediateFileDir, fromStep="PARSE", toStep="FIND-HEADS")
# Entity name splitting is omitted as this data may be used for predicting entities
preprocessor.process(xml, intermediateFileDir, omitSteps=["CONVERT", "SPLIT-SENTENCES", "NER", "SPLIT-NAMES", "DIVIDE-SETS"])
def parseXML(xml, intermediateFileDir, debug=False):
preprocessor = Preprocessor()
preprocessor.setArgForAllSteps("debug", debug)
preprocessor.stepArgs("PARSE")["requireEntities"] = False
#preprocessor.process(xml, intermediateFileDir, fromStep="SPLIT-SENTENCES", toStep="FIND-HEADS", omitSteps=["NER"])
#preprocessor.process(xml, intermediateFileDir, fromStep="PARSE", toStep="FIND-HEADS")
# Entity name splitting is omitted as this data may be used for predicting entities
preprocessor.process(xml,
intermediateFileDir,
omitSteps=[
"CONVERT", "SPLIT-SENTENCES", "NER",
"SPLIT-NAMES", "DIVIDE-SETS"
])
def parseXML(xml,
outStem,
intermediateFiles=True,
debug=False,
bbResources=False):
preprocessor = Preprocessor()
if bbResources:
preprocessor.insertStep(5, "BB_RESOURCES", insertResources.process, {},
"bb-resources.xml")
preprocessor.setArgForAllSteps("debug", debug)
preprocessor.stepArgs("PARSE")["requireEntities"] = False
if not intermediateFiles:
preprocessor.setNoIntermediateFiles()
preprocessor.process(xml, outStem, omitSteps=["NER", "DIVIDE-SETS"])
def convert(inPath, outDir, corpusId, directed, negatives, preprocess, preprocessorParameters=None, debug=False, clear=False, constParser="BLLIP-BIO", depParser="STANFORD-CONVERT", logging=True):
assert negatives in ("INCLUDE", "SKIP", "REVERSE_POS")
# Download the corpus if needed
if inPath == None:
if not hasattr(Settings, "SE10T8_CORPUS"):
SemEval2010Task8Tools.install()
inPath = Settings.SE10T8_CORPUS
assert os.path.exists(inPath)
# Prepare the output directory
if not os.path.exists(outDir):
print "Making output directory", outDir
os.makedirs(outDir)
elif clear:
print "Removing output directory", outDir
shutil.rmtree(outDir)
# Start logging
if logging:
Stream.openLog(os.path.join(outDir, "log.txt"), clear=clear)
# Read and process the corpus files
archive = zipfile.ZipFile(inPath, 'r')
usedIds = set()
tree = None
for fileName, setName in [("SemEval2010_task8_all_data/SemEval2010_task8_training/TRAIN_FILE.TXT", "train"),\
("SemEval2010_task8_all_data/SemEval2010_task8_testing_keys/TEST_FILE_FULL.TXT", "test")]:
print "Processing file", fileName, "as set", setName
f = archive.open(fileName)
tree = processLines(f.readlines(), setName, directed=directed, negatives=negatives, usedIds=usedIds, tree=tree, corpusId=corpusId)
f.close()
# Divide the training set into training and development sets
MakeSets.processCorpus(tree, None, "train", [("train", 0.7), ("devel", 1.0)], 1)
# Write out the converted corpus
convertedPath = os.path.join(outDir, corpusId + "-converted.xml")
ETUtils.write(tree.getroot(), convertedPath)
# Preprocess the converted corpus
if preprocess:
outPath = os.path.join(outDir, corpusId + ".xml")
preprocessor = Preprocessor(constParser, depParser)
preprocessor.setArgForAllSteps("debug", debug)
preprocessor.stepArgs("CONVERT")["corpusName"] = corpusId
preprocessor.process(convertedPath, outPath, preprocessorParameters, omitSteps=["SPLIT-SENTENCES", "NER", "SPLIT-NAMES"])
# Stop logging
if logging:
Stream.closeLog(os.path.join(outDir, "log.txt"))
def convert(inPath,
outDir,
corpusId,
directed,
negatives,
preprocess,
preprocessorParameters=None,
debug=False,
clear=False,
constParser="BLLIP-BIO",
depParser="STANFORD-CONVERT",
logging=True):
assert negatives in ("INCLUDE", "SKIP", "REVERSE_POS")
# Download the corpus if needed
if inPath == None:
if not hasattr(Settings, "SE10T8_CORPUS"):
SemEval2010Task8Tools.install()
inPath = Settings.SE10T8_CORPUS
assert os.path.exists(inPath)
# Prepare the output directory
if not os.path.exists(outDir):
print "Making output directory", outDir
os.makedirs(outDir)
elif clear:
print "Removing output directory", outDir
shutil.rmtree(outDir)
# Start logging
if logging:
Stream.openLog(os.path.join(outDir, "log.txt"), clear=clear)
# Read and process the corpus files
archive = zipfile.ZipFile(inPath, 'r')
usedIds = set()
tree = None
for fileName, setName in [("SemEval2010_task8_all_data/SemEval2010_task8_training/TRAIN_FILE.TXT", "train"),\
("SemEval2010_task8_all_data/SemEval2010_task8_testing_keys/TEST_FILE_FULL.TXT", "test")]:
print "Processing file", fileName, "as set", setName
f = archive.open(fileName)
tree = processLines(f.readlines(),
setName,
directed=directed,
negatives=negatives,
usedIds=usedIds,
tree=tree,
corpusId=corpusId)
f.close()
# Divide the training set into training and development sets
MakeSets.processCorpus(tree, None, "train", [("train", 0.7),
("devel", 1.0)], 1)
# Write out the converted corpus
convertedPath = os.path.join(outDir, corpusId + "-converted.xml")
ETUtils.write(tree.getroot(), convertedPath)
# Preprocess the converted corpus
if preprocess:
outPath = os.path.join(outDir, corpusId + ".xml")
preprocessor = Preprocessor(constParser, depParser)
preprocessor.setArgForAllSteps("debug", debug)
preprocessor.stepArgs("CONVERT")["corpusName"] = corpusId
preprocessor.process(
convertedPath,
outPath,
preprocessorParameters,
omitSteps=["SPLIT-SENTENCES", "NER", "SPLIT-NAMES"])
# Stop logging
if logging:
Stream.closeLog(os.path.join(outDir, "log.txt"))
