def load(self,
input,
dataSetNames=None,
corpusName=None,
output=None,
extensions=None):
if isinstance(input, basestring) and input.isdigit():
return self.downloadPubmed(input, output)
elif isinstance(input, basestring) and (os.path.isdir(input)
or input.endswith(".tar.gz")
or input.endswith(".txt")
or "," in input):
return self.convert(input,
dataSetNames,
corpusName,
output,
extensions=extensions)
elif isinstance(input, basestring) and not os.path.exists(input):
fullPath = os.path.join(Settings.CORPUS_DIR, input)
print >> sys.stderr, "Loading installed corpus from", fullPath
if os.path.exists(fullPath):
return ETUtils.ETFromObj(fullPath)
else:
#setPaths = [fullPath + x for x in ("-train.xml", "-devel.xml", "-test.xml")]
pattern = input + ".+\.xml" #"|".join([input + x for x in ("-train.xml", "-devel.xml", "-test.xml")])
matching = Utils.InteractionXML.MergeSets.getMatchingFiles(
pattern, Settings.CORPUS_DIR)
if len(matching) == 0:
matching = Utils.InteractionXML.MergeSets.getMatchingFiles(
pattern)
if len(matching
) > 0: #any([os.path.exists(x) for x in setPaths]):
return Utils.InteractionXML.MergeSets.mergeSets(pattern)
else:
raise Exception("Cannot find input '" + str(input) + "'")
elif isinstance(input, dict):
return Utils.InteractionXML.MergeSets.mergeSets(pattern)
else:
print >> sys.stderr, "Processing source as interaction XML"
return ETUtils.ETFromObj(input)
def convertXML(xml, outPath):
xml = ETUtils.ETFromObj(xml)
corpusObj = {"name": None, "children": []}
root = xml.getroot()
for document in root.getiterator("document"):
docObj = addChild(corpusObj, document)
for sentence in document.getiterator("sentence"):
sentObj = addChild(docObj, sentence)
for elType in ("entity", "interaction"):
for element in sentence.getiterator(elType):
addChild(sentObj, element)
with open(outPath, "wt") as f:
json.dump(corpusObj, f, indent=2, cls=IJSONEncoder)
def validateCorpus(input, output, strict=True):
print >> sys.stderr, "Validating XML"
print >> sys.stderr, "Loading corpus file", input
corpusTree = ETUtils.ETFromObj(input)
corpusRoot = corpusTree.getroot()
counts = validate(corpusRoot, strict)
print >> sys.stderr, "Corpus validated:", dict(counts)
if output != None:
print >> sys.stderr, "Writing output to", output
ETUtils.write(corpusRoot, output)
return corpusTree
def negateEvents(input, output=None, verbose=False):
if not (ET.iselement(input) and input.tag == "sentence"):
print >> sys.stderr, "Loading corpus file", input
corpusTree = ETUtils.ETFromObj(input)
corpusRoot = corpusTree.getroot()
if not (ET.iselement(input) and input.tag == "sentence"):
sentences = corpusRoot.getiterator("sentence")
else:
sentences = [input]
counts = defaultdict(int)
for sentence in sentences:
for entity in sentence.findall("entity"):
counts["all-entities"] += 1
eType = entity.get("type")
if not isNegatableEPITrigger(eType):
counts["out-of-scope"] += 1
continue
eBaseType = getEPIBaseType(eType)
eText = entity.get("text").lower()
eNewType = determineNewType(eType, eText)
# Insert changed charOffset
counts["entities"] += 1
if verbose:
print "Entity", entity.get("id"), [entity.get("text")], [
eType, eBaseType, eNewType
],
if eNewType != eBaseType:
counts["negated"] += 1
if verbose: print "NEGATED",
if eNewType == eType:
counts["correct"] += 1
if verbose: print "CORRECT"
else:
counts["incorrect"] += 1
if eNewType == eBaseType:
counts["incorrect-pos"] += 1
else:
counts["incorrect-neg"] += 1
if verbose: print "INCORRECT"
entity.set("type", eNewType)
if verbose:
print counts
if not (ET.iselement(input) and input.tag == "sentence"):
if output != None:
print >> sys.stderr, "Writing output to", output
ETUtils.write(corpusRoot, output)
return corpusTree
def makeDDI13SubmissionFile(input, output, mode="interactions", idfilter=None):
xml = ETUtils.ETFromObj(input)
outFile = open(output, "wt")
for sentence in xml.getiterator("sentence"):
sentenceId = sentence.get("id")
if idfilter != None and idfilter not in sentenceId:
continue
# Output entities
if mode == "entities":
for entity in sentence.findall("entity"):
if entity.get("type") != "neg":
outFile.write(sentenceId)
offsets = Range.charOffsetToTuples(
entity.get("charOffset"))
for i in range(len(offsets)):
offsets[i] = (offsets[i][0], offsets[i][1] - 1)
outFile.write(
"|" + Range.tuplesToCharOffset(offsets, rangeSep=";"))
outFile.write("|" + entity.get("text"))
outFile.write("|" + entity.get("type"))
outFile.write("\n")
if mode == "interactions":
# First determine which pairs interact
intMap = defaultdict(lambda: defaultdict(lambda: None))
for interaction in sentence.findall("interaction"):
# Make mapping both ways to discard edge directionality. This isn't actually needed,
# since MultiEdgeExampleBuilder builds entity pairs in the same order as this function,
# but shouldn't harm to include it and now it works regardless of pair direction.
if interaction.get("type") != "neg" and interaction.get(
"given") != "True":
intMap[interaction.get("e1")][interaction.get(
"e2")] = interaction
intMap[interaction.get("e2")][interaction.get(
"e1")] = interaction
# Then write all pairs to the output file
entities = sentence.findall("entity")
for i in range(0, len(entities) - 1):
for j in range(i + 1, len(entities)):
eIId = entities[i].get("id")
eJId = entities[j].get("id")
outFile.write(sentenceId + "|" + eIId + "|" + eJId + "|")
if intMap[eIId][eJId] != None:
interaction = intMap[eIId][eJId]
assert interaction.get("type") != "neg"
outFile.write("1|" + interaction.get("type") + "\n")
else:
outFile.write("0|null\n")
outFile.close()
def processCorpus(inputFilename, outputFilename, rules, reverse=False):
print >> sys.stderr, "Deleting elements, rules =", rules
print >> sys.stderr, "Loading corpus file", inputFilename
corpusTree = ETUtils.ETFromObj(inputFilename)
corpusRoot = corpusTree.getroot()
countsByType = defaultdict(int)
removeElements(corpusRoot, rules, reverse, countsByType)
print >> sys.stderr, "Deleted elements"
for k in sorted(countsByType.keys()):
print >> sys.stderr, " " + k + ":", countsByType[k]
if outputFilename != None:
print >> sys.stderr, "Writing output to", outputFilename
ETUtils.write(corpusRoot, outputFilename)
return corpusTree
def loadCorpus(filename,
parse=None,
tokenization=None,
removeIntersentenceInteractions=True,
removeNameInfo=False):
try:
import xml.etree.cElementTree as ET
except ImportError:
import cElementTree as ET
import sys, gzip
if type(filename) == types.StringType:
print >> sys.stderr, "Loading corpus file", filename
corpusTree = ETUtils.ETFromObj(filename)
corpusRoot = corpusTree.getroot()
return CorpusElements(corpusRoot, parse, tokenization,
removeIntersentenceInteractions, corpusTree,
removeNameInfo)
def getTriggers(corpus):
"""
Returns a dictionary of "entity type"->"entity text"->"count"
"""
corpus = ETUtils.ETFromObj(corpus)
trigDict = {}
for entity in corpus.getroot().getiterator("entity"):
if entity.get("given") == "True":
continue
eType = entity.get("type")
if not trigDict.has_key(eType):
trigDict[eType] = {}
eText = entity.get("text")
eText = PorterStemmer.stem(eText)
if not trigDict[eType].has_key(eText):
trigDict[eType][eText] = 0
trigDict[eType][eText] += 1
return trigDict
def mergeSets(input, corpusDir=None, output=None, allowNone=False):
# Find the files
if isinstance(input, dict):
filenames = [{"path":input[x], "set":x} for x in input]
else:
if corpusDir == None:
if os.path.dirname(input):
corpusDir = os.path.dirname(input)
input = os.path.basename(input)
else:
corpusDir = os.path.normpath(Settings.DATAPATH + "/corpora")
print >> sys.stderr, "Searching for corpus files at " + corpusDir + " using pattern " + input
filenames = [{"path":os.path.join(corpusDir, x), "set":None} for x in getMatchingFiles(input, corpusDir)]
# Merge the files
print >> sys.stderr, "Merging input files", filenames
if len(filenames) == 0:
if allowNone:
print >> sys.stderr, "Nothing to merge"
return
else:
raise Exception("No input files found for merging")
newRoot = None
counts = defaultdict(int)
for filename in filenames:
print >> sys.stderr, "Merging file", filename["path"]
xml = ETUtils.ETFromObj(filename["path"]).getroot()
if newRoot == None:
newRoot = ET.Element("corpus", xml.attrib)
else:
assert newRoot.attrib == xml.attrib
for doc in xml.iter("document"):
assert doc.get("set") != None, doc.attrib
if filename["set"] != None:
assert filename["set"] == doc.get("set")
counts["set=" + doc.get("set")] += 1
counts["set(" + filename["path"] + ")=" + doc.get("set")] += 1
for element in xml:
newRoot.append(element)
print >> sys.stderr, dict(counts)
if output != None:
print "Writing merged corpus to", output
ETUtils.write(newRoot, output)
return ET.ElementTree(newRoot)
def mixSets(input, output, docOrigIds, sourceSet, targetSet):
print >> sys.stderr, "Mixing Sets", input
corpusTree = ETUtils.ETFromObj(input)
corpusRoot = corpusTree.getroot()
if docOrigIds != None:
for document in corpusRoot.getiterator("document"):
docId = document.get("pmid")
if docId == None:
docId = document.get("origId")
if docId in docOrigIds:
assert document.get("set") == sourceSet
document.set("set", targetSet)
docOrigIds.remove(docId)
assert len(docOrigIds) == 0, docOrigIds
sentenceIds = None
if sentenceIds != None:
for document in corpusRoot.getiterator("document"):
removed = []
for sentence in document.findall("sentence"):
assert document.get("set") == sourceSet
sentenceId = sentence.get("id")
if sentenceId in sentenceIds:
removed.append(document.remove(sentence))
sentenceIds.remove(sentenceId)
if len(removed) > 0:
newDoc = ET.Element("document")
for attr in document.attrib:
newDoc.set(attr, document.get(attr))
newDoc.set("id", None)
newDoc.set("set", targetSet)
for sentence in removed:
newDoc.append(sentence)
corpusRoot.append(newDoc)
assert len(sentenceIds) == None
RecalculateIds.recalculateIds(corpusTree, onlyWithinSentence=False)
if output != None:
print >> sys.stderr, "Writing output to", output
ETUtils.write(corpusRoot, output)
return corpusTree
def processCorpus(input, output, rules):
print >> sys.stderr, "Deleting attributes, rules =", rules
print >> sys.stderr, "Loading corpus file", input
corpusTree = ETUtils.ETFromObj(input)
corpusRoot = corpusTree.getroot()
countsByType = {}
for key in sorted(rules.keys()):
for attribute in rules[key]:
countsByType[key + ":" + attribute] = 0
removeAttributes(corpusRoot, key, rules[key], countsByType)
print >> sys.stderr, "Removed"
for k in sorted(countsByType.keys()):
print >> sys.stderr, " " + k + ":", countsByType[k]
if output != None:
print >> sys.stderr, "Writing output to", output
ETUtils.write(corpusRoot, output)
return corpusTree
def processCorpus(input, output, rules):
if rules == None:
raise Exception("No mapping rules defined")
elif isinstance(rules, basestring):
rules = eval(rules)
print >> sys.stderr, "Mapping attributes, rules =", rules
print >> sys.stderr, "Loading corpus file", input
corpusTree = ETUtils.ETFromObj(input)
corpusRoot = corpusTree.getroot()
counts = defaultdict(int)
for key in sorted(rules.keys()):
mapAttributes(corpusRoot, key, rules[key], counts)
print >> sys.stderr, "Mapped", dict(counts)
if output != None:
print >> sys.stderr, "Writing output to", output
ETUtils.write(corpusRoot, output)
return corpusTree
def parseStats(input):
print >> sys.stderr, "Loading input file", input
inputTree = ETUtils.ETFromObj(input)
inputRoot = inputTree.getroot()
counts = defaultdict(int)
for sentence in inputRoot.getiterator("sentence"):
counts["sentence"] += 1
analysesElement = sentence.find("sentenceanalyses")
if analysesElement == None:
counts["sentence-no-analyses"] += 1
continue
# Create parses element (if needed)
parsesElement = analysesElement.find("parses")
if parsesElement == None:
counts["sentence-no-parses"] += 1
continue
# Loop through parses
for parseElement in parsesElement:
parserName = parseElement.get("parser")
counts["parse:" + parserName] += 1
if parseElement.get("pennstring") in ["", None]:
counts["parse:" + parserName + "(no penn)"] += 1
if len(parseElement.findall("dependency")) == 0:
counts["parse:" + parserName + "(no dependencies)"] += 1
if len(parseElement.findall("phrase")) == 0:
counts["parse:" + parserName + "(no phrases)"] += 1
# Tokenizations
tokenizationsElement = analysesElement.find("tokenizations")
if tokenizationsElement == None:
counts["sentence-no-tokenizations"] += 1
continue
# Loop through tokenizations
for tokenizationElement in tokenizationsElement:
tokenizerName = tokenizationElement.get("tokenizer")
counts["tokenization:" + tokenizerName] += 1
if len(tokenizationElement.findall("token")) == 0:
counts["tokenization:" + tokenizerName + "(no tokens)"] += 1
print >> sys.stderr, "Parse statistics for", input
for key in sorted(counts.keys()):
print >> sys.stderr, " ", key + ":", counts[key]
def visualize(inPath, outPath, sentId, parseName):
setSVGOptions()
xml = ETUtils.ETFromObj(inPath)
sentences = {x.get("id"):x for x in xml.iter("sentence")}
if sentId not in sentences:
print >> sys.stderr, "Sentence id '" + sentId + "' not found"
return
sentence = sentences[sentId]
parse = IXMLUtils.getParseElement(sentence, parseName)
if not parse:
print >> sys.stderr, "Sentence has no parse with name '" + parseName + "'"
return
tokenization = IXMLUtils.getTokenizationElement(sentence, parse.get("tokenizer"))
graph = SentenceGraph(sentence, [x for x in tokenization.findall("token")], [x for x in parse.findall("dependency")])
graph.mapInteractions([x for x in sentence.findall("entity")], [x for x in sentence.findall("interaction")])
svgTokens = tokensToSVG(tokenization.findall("token"))
svgEdges = edgesToSVG(svgTokens, graph)
#writeSVG({x.id:x for x in svgTokens}, svgEdges, outPath)
writeSVG(svgTokens, svgEdges, outPath)
def catenateElements(inputs, inputDir):
print >> sys.stderr, "##### Catenate interaction XML as elements #####"
output = {}
for dataSet in ("devel", "train"):
root = ET.Element("corpus", {"source": ",".join(inputs)})
tree = ET.ElementTree(root)
print "Processing corpus dataset", dataSet
output[dataSet] = tree
for input in inputs:
corpusPath = os.path.join(inputDir, input + "-" + dataSet + ".xml")
print >> sys.stderr, "Catenating", corpusPath
if not os.path.exists(corpusPath):
print "Input", corpusPath, "not found"
continue
xml = ETUtils.ETFromObj(corpusPath)
for document in xml.getiterator("document"):
root.append(document)
RecalculateIds.recalculateIds(tree)
return output
def findHeads(corpus, stringsFrom, methods, parse, tokenization):
for m in methods:
assert m in ["REMOVE", "SYNTAX", "DICT"]
corpus = ETUtils.ETFromObj(corpus)
counts = {}
for method in methods:
print >> sys.stderr, method, "pass"
if method == "REMOVE":
counts[method] = removeHeads(corpus)
elif method == "DICT":
counts[method] = findHeadsDictionary(corpus, stringsFrom, parse,
tokenization)
elif method == "SYNTAX":
counts[method] = findHeadsSyntactic(corpus, parse, tokenization)
print >> sys.stderr, method, "pass added", counts[method][
0], "and removed", counts[method][1], "heads"
print >> sys.stderr, "Summary (pass/added/removed):"
for method in methods:
print >> sys.stderr, " ", method, "/", counts[method][0], "/", counts[
method][1]
def toSTFormat(input,
output=None,
outputTag="a2",
useOrigIds=False,
debug=False,
skipArgs=[],
validate=True,
writeExtra=False,
allAsRelations=False):
print >> sys.stderr, "Loading corpus", input
corpusTree = ETUtils.ETFromObj(input)
print >> sys.stderr, "Corpus file loaded"
corpusRoot = corpusTree.getroot()
nonEntitySiteCount = 0
documents = []
for document in corpusRoot.findall("document"):
stDoc = Document()
stDoc.id = document.get("pmid")
if stDoc.id == None:
stDoc.id = document.get("origId")
addTextToSTDoc(stDoc, document)
documents.append(stDoc)
eMap = {}
tMap = {}
entityElementMap = {} # for task 3
addEntitiesToSTDoc(stDoc, document, tMap, eMap, entityElementMap,
useOrigIds)
addInteractionsToSTDoc(stDoc, document, tMap, eMap, entityElementMap,
skipArgs, allAsRelations)
if output != None:
print >> sys.stderr, "Writing output to", output
writeSet(documents,
output,
resultFileTag=outputTag,
debug=debug,
writeExtra=writeExtra)
return documents
def splitMergedElements(inputFilename, outputFilename=None):
print >> sys.stderr, "##### Split elements with merged types #####"
print >> sys.stderr, "Loading corpus", inputFilename
corpusTree = ETUtils.ETFromObj(inputFilename)
corpusRoot = corpusTree.getroot()
documents = corpusRoot.findall("document")
counter = ProgressCounter(len(documents), "Documents")
countsByType = {"entity": [0, 0], "interaction": [0, 0], "pair": [0, 0]}
for document in documents:
counter.update()
for sentence in document.findall("sentence"):
processSentence(sentence, countsByType)
print >> sys.stderr, "Results"
for k in sorted(countsByType.keys()):
print >> sys.stderr, " " + k + ": removed", countsByType[k][
0], "created", countsByType[k][1]
if outputFilename != None:
print >> sys.stderr, "Writing output to", outputFilename
ETUtils.write(corpusRoot, outputFilename)
return corpusTree
def convertAndEvaluate(xml, task, a2Tag, goldDir=None, debug=False):
print >> sys.stderr, "Loading corpus", input
corpusTree = ETUtils.ETFromObj(xml)
print >> sys.stderr, "Corpus file loaded"
corpusRoot = corpusTree.getroot()
if task == None:
task = corpusRoot.get("source")
print >> sys.stderr, "BioNLP Shared Task evaluation for task", task
tempdir = tempfile.mkdtemp()
subTasks = None
if "." in task:
mainTask, subTasks = task.split(".")
subTasks = [int(x) for x in subTasks]
if subTasks != None:
for subTask in subTasks:
skipArgs = []
skipModifiers = False
if subTask == 1:
skipArgs = ['AtLoc', 'ToLoc', 'Site']
skipModifiers = True
elif subTask == 2:
skipModifiers = True
print >> sys.stderr, "---------------", "Converting task", task, "corpus for GENIA sub task", subTask, "---------------"
print >> sys.stderr, "Skipping arguments:", skipArgs, " Skipping modifiers:", skipModifiers
outDir = os.path.join(tempdir, "events-" + str(subTask))
Utils.STFormat.ConvertXML.toSTFormat(corpusTree,
outDir,
outputTag=a2Tag,
skipArgs=skipArgs,
skipModifiers=skipModifiers)
evaluate(outDir, mainTask + "." + str(subTask), goldDir, debug)
else:
outDir = os.path.join(tempdir, "events")
print >> sys.stderr, "---------------", "Converting task", task, "corpus", "---------------"
Utils.STFormat.ConvertXML.toSTFormat(xml, outDir, outputTag=a2Tag)
evaluate(outDir, task, goldDir, debug)
shutil.rmtree(tempdir)
return xml
def loadDrugBank(filename, preTag="{http://drugbank.ca}", verbose=False):
data = defaultdict(lambda : defaultdict(list))
print "Loading DrugBank XML"
xml = ETUtils.ETFromObj(filename)
print "Processing DrugBank XML"
root = xml.getroot()
assert root.tag == preTag+"drugs", root.tag
for drug in root.findall(preTag+"drug"):
id = drug.find(preTag+"drugbank-id").text
name = drug.find(preTag+"name").text
if verbose: print id, name
assert id not in data
data[id]["name"] = name
# TODO: Enzymes & targets
# TODO: hydrophobicity
getNestedItems(drug, "synonym", data[id], preTag)
getNestedItems(drug, "brand", data[id], preTag)
getNestedItems(drug, "group", data[id], preTag)
getNestedItems(drug, "category", data[id], preTag, "categories")
interactions = drug.find(preTag+"drug-interactions").findall(preTag+"drug-interaction")
for interaction in interactions:
data[id]["interaction"].append( [interaction.find(preTag+"drug").text, None, interaction.find(preTag+"description").text,] )
return data
def getHeads(corpus):
corpus = ETUtils.ETFromObj(corpus)
headDict = {}
headDict["None"] = {}
for sentence in corpus.getiterator("sentence"):
headOffsetStrings = set()
for entity in sentence.findall("entity"):
eType = entity.get("type")
if not headDict.has_key(eType):
headDict[eType] = {}
eText = entity.get("text")
headOffset = entity.get("headOffset")
headOffsetStrings.add(headOffset)
headOffset = Range.charOffsetToSingleTuple(headOffset)
charOffset = Range.charOffsetToSingleTuple(
entity.get("charOffset"))
if headOffset == charOffset:
if not headDict[eType].has_key(eText):
headDict[eType][eText] = 0
headDict[eType][eText] += 1
else:
headText = sentenceText[headOffset[0] -
charOffset[0]:headOffset[1] -
charOffset[0] + 1]
if not headDict[eType].has_key(headText):
headDict[eType][headText] = 0
headDict[eType][headText] += 1
for token in tokens:
if not token.get(
"charOffset"
) in headOffsetStrings: # token is not the head of any entity
headText = token.get("text")
if not headDict["None"].has_key(headText):
headDict["None"][headText] = 0
headDict["None"][headText] += 1
return headDict
def analyze(self, inputs, model=None, verbose=False):
self._init()
if type(inputs) in types.StringTypes or not isinstance(
inputs, collections.Sequence):
inputs = [inputs]
for xml in inputs:
print >> sys.stderr, "Analyzing", xml
xml = ETUtils.ETFromObj(xml)
for document in xml.getiterator("document"):
# Collect elements into dictionaries
entityById = {}
for entity in document.getiterator("entity"):
entityById[entity.get("id")] = entity
interactions = []
interactionsByE1 = defaultdict(list)
for interaction in document.getiterator("interaction"):
interactions.append(interaction)
interactionsByE1[interaction.get("e1")].append(interaction)
siteOfTypes = self.buildSiteOfMap(interactions,
interactionsByE1, entityById)
# Add entity elements to analysis
for entity in document.getiterator("entity"):
self.addEntityElement(entity, interactionsByE1)
# Add interaction elements to analysis
for interaction in interactions:
self.addInteractionElement(interaction, entityById,
siteOfTypes[interaction])
# Calculate event definition argument limits from event instances
for event in self.events.values():
event.countArguments()
self._updateSupportingAnalyses()
if verbose:
print >> sys.stderr, self.toString()
if model != None:
self.save(model)
def makeSubset(input, output=None, ratio=1.0, seed=0, invert=False):
if ratio == 1.0:
if output != None:
shutil.copy2(input, output)
return output
else:
return input
print >> sys.stderr, "====== Making subset ======"
print >> sys.stderr, "Subset for ", input, "ratio", ratio, "seed", seed
xml = ETUtils.ETFromObj(input).getroot()
count = 0
sentCount = 0
for document in xml.findall("document"):
sentCount += len(document.findall("sentence"))
count += 1
totalFolds = min(100, count)
selectedFolds = int(ratio * min(100, count))
division = Core.Split.getFolds(count, totalFolds, seed)
#print division, selectedFolds - 1
index = 0
removeCount = 0
sentRemoveCount = 0
for document in xml.findall("document"):
removal = division[index] > selectedFolds - 1
if invert:
removal = not removal
if removal:
xml.remove(document)
sentRemoveCount += len(document.findall("sentence"))
removeCount += 1
index += 1
print >> sys.stderr, "Subset", "doc:", count, "removed:", removeCount, "sent:", sentCount, "sentremoved:", sentRemoveCount
xml.set("subsetRatio", str(ratio))
xml.set("subsetSeed", str(seed))
if output != None:
ETUtils.write(xml, output)
return output
def fixAltOffsets(input, output=None):
print >> sys.stderr, "Loading corpus", input
corpusTree = ETUtils.ETFromObj(input)
print >> sys.stderr, "Corpus file loaded"
corpusRoot = corpusTree.getroot()
docCount = 0
sentencesCreated = 0
sentences = [x for x in corpusRoot.getiterator("sentence")]
counter = ProgressCounter(len(sentences), "FixAltOffsets")
fixCount = 0
# fix spans
for sentence in sentences:
counter.update(
1, "Fixing AltOffsets for sentence (" + sentence.get("id") + "): ")
sentOffset = Range.charOffsetToSingleTuple(sentence.get("charOffset"))
for entity in sentence.findall("entity"):
altOffsetString = entity.get("altOffset")
if altOffsetString == None:
continue
#print altOffsetString
altOffsets = Range.charOffsetToTuples(altOffsetString)
assert len(altOffsets) == 1
for i in range(len(altOffsets)):
altOffset = altOffsets[i]
altOffsets[i] = (altOffset[0] - sentOffset[0],
altOffset[1] - sentOffset[0])
entity.set("altOffset", Range.tuplesToCharOffset(altOffsets))
fixCount += 1
print >> sys.stderr, "Fixed", fixCount, "altOffsets"
if output != None:
print >> sys.stderr, "Writing output to", output
ETUtils.write(corpusRoot, output)
return corpusTree
def writeXML(self,
examples,
predictions,
corpus,
outputFile,
classSet=None,
parse=None,
tokenization=None,
goldCorpus=None,
exampleStyle=None,
structureAnalyzer=None):
"""
Writes task 3 examples to interaction XML. Assumes task 3 classification
is done with SVMMulticlass Classifier, used for two classes.
"""
print >> sys.stderr, "Adding task 3 to Interaction XML"
examples, predictions = self.loadExamples(examples, predictions)
if type(classSet) == types.StringType: # class names are in file
classSet = IdSet(filename=classSet)
classIds = None
if classSet != None:
classIds = classSet.getIds()
corpusTree = ETUtils.ETFromObj(corpus)
corpusRoot = corpusTree.getroot()
# Determine subtask
task3Type = None
for example in examples:
assert example[3].has_key("t3type")
task3Type = example[3]["t3type"]
break
if task3Type == None:
if outputFile != None:
print >> sys.stderr, "Writing corpus to", outputFile
ETUtils.write(corpusRoot, outputFile)
return corpusTree
assert task3Type in ["multiclass", "speculation", "negation"]
# Remove the task 3 subtask information if it already exists
for entity in corpusRoot.getiterator("entity"):
if task3Type == "multiclass":
entity.set("speculation", "False")
entity.set("negation", "False")
elif task3Type == "speculation":
entity.set("speculation", "False")
else: # task3Type == "negation"
entity.set("negation", "False")
specMap = {}
negMap = {}
for example, prediction in itertools.izip(examples, predictions):
assert example[3]["xtype"] == "task3"
if example[3]["t3type"] == "multiclass":
if isinstance(prediction, dict):
encoded = prediction["prediction"]
predictedModifiers = [
classSet.getName(i) for i in range(len(encoded))
if encoded[i] == 1
]
else:
predictedClassName = classSet.getName(prediction[0])
predictedModifiers = ""
if predictedClassName != "neg":
predictedModifiers = predictedClassName.split("---")
if "negation" in predictedModifiers:
assert not negMap.has_key(example[3]["entity"])
negMap[example[3]["entity"]] = (True, prediction)
if "speculation" in predictedModifiers:
assert not specMap.has_key(example[3]["entity"])
specMap[example[3]["entity"]] = (True, prediction)
else:
if example[3]["t3type"] == "speculation":
map = specMap
else:
map = negMap
if prediction[0] != 1:
assert not map.has_key(example[3]["entity"])
map[example[3]["entity"]] = (True, prediction)
else:
assert not map.has_key(example[3]["entity"])
map[example[3]["entity"]] = (False, prediction)
for entity in corpusRoot.getiterator("entity"):
eId = entity.get("id")
if task3Type == "multiclass":
if specMap.has_key(eId):
entity.set("speculation", str(specMap[eId][0]))
entity.set(
"modConf",
self.getPredictionStrengthString(
specMap[eId][1], classSet, classIds))
if negMap.has_key(eId):
entity.set("negation", str(negMap[eId][0]))
entity.set(
"modConf",
self.getPredictionStrengthString(
negMap[eId][1], classSet, classIds))
else:
if task3Type == "speculation":
if specMap.has_key(eId):
entity.set("speculation", str(specMap[eId][0]))
entity.set(
"specConf",
self.getPredictionStrengthString(
specMap[eId][1], classSet, classIds, [""]))
elif task3Type == "negation":
if negMap.has_key(eId):
entity.set("negation", str(negMap[eId][0]))
entity.set(
"negConf",
self.getPredictionStrengthString(
negMap[eId][1], classSet, classIds,
["", "speculation"]))
# Write corpus
if outputFile != None:
print >> sys.stderr, "Writing corpus to", outputFile
ETUtils.write(corpusRoot, outputFile)
return corpusTree
optparser.add_option("-d",
"--debug",
default=False,
action="store_true",
dest="debug",
help="Debug mode")
optparser.add_option("-v",
"--validate",
default=None,
dest="validate",
help="validate input",
metavar="FILE")
(options, args) = optparser.parse_args()
s = StructureAnalyzer()
if options.load:
s.load(None, options.input)
else:
s.analyze(options.input.split(","))
print >> sys.stderr, "--- Structure Analysis ----"
print >> sys.stderr, s.toString()
if options.validate != None:
print >> sys.stderr, "--- Validation ----"
xml = ETUtils.ETFromObj(options.validate)
s.validate(xml, simulation=False, debug=options.debug)
if options.output != None:
ETUtils.write(xml, options.output)
elif options.output != None:
print >> sys.stderr, "Structure analysis saved to", options.output
s.save(None, options.output)
def processCorpus(input, parserName):
print >> sys.stderr, "Loading corpus file", input
corpusRoot = ETUtils.ETFromObj(input).getroot()
documents = corpusRoot.findall("document")
counts = defaultdict(int)
matchByType = defaultdict(lambda: [0, 0])
filteredMatchByType = defaultdict(lambda: [0, 0])
filter = set(
["NP", "TOK-tIN", "WHADVP", "WHNP", "TOK-tWP$", "TOK-tPRP$", "NP-IN"])
# # fix spans
# for document in documents:
# for sentence in document.findall("sentence"):
# sentOffset = Range.charOffsetToSingleTuple(sentence.get("charOffset"))
# for entity in sentence.findall("entity"):
# altOffsetString = entity.get("altOffset")
# if altOffsetString == None:
# continue
# #print altOffsetString
# altOffsets = Range.charOffsetToTuples(altOffsetString)
# assert len(altOffsets) == 1
# for i in range(len(altOffsets)):
# altOffset = altOffsets[i]
# altOffsets[i] = (altOffset[0] - sentOffset[0], altOffset[1] - sentOffset[0])
# entity.set("altOffset", Range.tuplesToCharOffset(altOffsets))
#counter = ProgressCounter(len(documents), "Documents")
for document in documents:
for sentence in document.findall("sentence"):
entities = sentence.findall("entity")
parse = ETUtils.getElementByAttrib(
sentence.find("sentenceanalyses"), "parse",
{"parser": parserName})
if parse == None:
continue
tokenization = ETUtils.getElementByAttrib(
sentence.find("sentenceanalyses"), "tokenization",
{"tokenizer": parse.get("tokenizer")})
phrases, phraseDict = makePhrases(parse, tokenization, entities)
phraseOffsets = phraseDict.keys()
#phraseOffsets.sort()
phraseNECounts = getNECounts(phrases, entities)
for value in phraseDict.values():
counts["phrases"] += len(value)
for phrase in value:
matchByType[phrase.get("type")][0] += 1
if phrase.get("type") in filter:
filteredMatchByType[phrase.get("type")][0] += 1
counts["phrases-filtered"] += 1
if phrase.get("type").find("NP") != -1:
matchByType[phrase.get("type") + "_NE" +
str(phraseNECounts[phrase])][0] += 1
counts["tokens"] += len(tokenization.findall("token"))
corefType = {}
for interaction in sentence.findall("interaction"):
if interaction.get("type") == "Coref":
corefType[interaction.get("e1")] = "Anaphora"
corefType[interaction.get("e2")] = "Antecedent"
for entity in entities:
if entity.get("given") == "True":
continue
counts["entity"] += 1
print "entity", entity.get("id")
print ETUtils.toStr(entity)
matches = getMatchingPhrases(entity, phraseOffsets, phraseDict)
count = 0
filteredCount = 0
for phrase in matches:
cType = "UNKNOWN"
if corefType.has_key(entity.get("id")):
cType = corefType[entity.get("id")]
print " match", count, ETUtils.toStr(phrase), "NE" + str(
phraseNECounts[phrase]
), "ctype:" + cType, "ent:" + ETUtils.toStr(entity)
count += 1
matchByType[phrase.get("type")][1] += 1
matchByType[phrase.get("type") + "_" + cType][1] += 1
matchByType[phrase.get("type") + "_" + cType + "_NE" +
str(phraseNECounts[phrase])][1] += 1
if phrase.get("type") in filter:
filteredCount += 1
filteredMatchByType[phrase.get("type")][1] += 1
# Matching
if count == 0:
print " NO MATCH", ETUtils.toStr(entity)
counts["no-match"] += 1
else:
counts["match"] += 1
# Multimatching
if len(matches) > 1:
bestMatch = selectBestMatch(entity, matches)
print " MULTIMATCH(" + entity.get(
"charOffset"
) + "," + str(entity.get("altOffset")) + ")", ", ".join([
x.get("type") + "_" + x.get("charOffset")
for x in matches
]), "SEL(" + bestMatch.get("type") + "_" + bestMatch.get(
"charOffset") + ")"
# Filtered matching
if filteredCount == 0: counts["no-match-filtered"] += 1
else: counts["match-filtered"] += 1
print "Match"
for key in sorted(matchByType.keys()):
print " ", key, " ", matchByType[key]
print "Filtered", filteredMatchByType
print "Counts", counts
def mainFunc(input,
output=None,
parseName="McCC",
tokenizationName=None,
newParseName=None,
newTokenizationName=None,
logFileName=None,
removeOld=True):
print >> sys.stderr, "Protein Name Splitter"
if logFileName != None:
print >> sys.stderr, "Writing log to", logFileName
logFile = open(logFileName, "wt")
else:
logFile = None
#if input.endswith(".gz"):
# inFile = gzip.GzipFile(input)
#else:
# inFile = open(input)
tree = ETUtils.ETFromObj(input)
if tokenizationName == None:
tokenizationName = parseName
#tree = ElementTree.parse(inFile)
root = tree.getroot()
sentences = [x for x in root.getiterator("sentence")]
counter = ProgressCounter(len(sentences), "Split Protein Names")
counter.showMilliseconds = True
missingTokCount = 0
for sentence in sentences:
sId = sentence.get("id")
counter.update(1, "Splitting names (" + sId + "): ")
tok = getTokenization(tokenizationName,
sentence,
sId,
remove=removeOld)
if tok == None:
missingTokCount += 1
continue
assert tok is not None, "Missing tokenization '%s' in sentence %s!" % (
tokenizationName, sId)
parse = getParse(parseName,
tokenizationName,
sentence,
sId,
remove=removeOld)
assert parse is not None, "Missing parse '%s' in sentence %s!" % (
parseName, sId)
split = splitTokens(tok, sentence, logFile)
# Default names
if removeOld:
if newTokenizationName == None:
newTokenizationName = tok.get("tokenizer")
if newParseName == None:
newParseName = parse.get("parser")
else:
if newTokenizationName == None:
newTokenizationName = "split-" + tok.get("tokenizer")
if newParseName == None:
newParseName = "split-" + parse.get("parser")
# add a new tokenization with the split tokens.
splittok = addTokenization(newTokenizationName, sentence, sId)
addTokensToTree(split, splittok)
for a in tok.attrib:
if splittok.get(a) == None:
splittok.set(a, tok.get(a))
#splittok.set("split-")
# make a mapping from original to split token ids. Store the
# head token when given.
tokenIdMap = {}
for t in split:
if t.head:
head = t.head
# traverse
while head.head is not None:
assert head.head != t, "Cyclic heads"
head = head.head
# should match (nah, punctuation problems)
# assert t.origId not in tokenIdMap or tokenIdMap[t.origId] == head.id, "Head conflict"
tokenIdMap[t.origId] = head.id
else:
# only allow overwrite of existing entry if the current token
# is not punctuation.
if t.origId not in tokenIdMap or not t.isPunct():
tokenIdMap[t.origId] = t.id
# make a copy of the specified parse that refers to the split tokens
# instead of the originals.
newparse = addParse(newParseName, newTokenizationName, sentence, sId)
for a in parse.attrib:
if newparse.get(a) == None:
newparse.set(a, parse.get(a))
newparse.set("ProteinNameSplitter", "True")
splittok.set("ProteinNameSplitter", "True")
depSeqId = 0 #1
for d in parse.getiterator("dependency"):
t1, t2, dType = d.get("t1"), d.get("t2"), d.get("type")
assert t1 in tokenIdMap and t2 in tokenIdMap, "INTERNAL ERROR"
dep = ElementTree.SubElement(newparse, "dependency")
dep.set("t1", tokenIdMap[t1])
dep.set("t2", tokenIdMap[t2])
dep.set("type", dType)
dep.set("id", "sd_%d" % depSeqId)
depSeqId += 1
# Add in new dependencies between the split parts.
for t in [tok for tok in split if tok.head is not None]:
dep = ElementTree.SubElement(newparse, "dependency")
dep.set("t1", t.head.id)
dep.set("t2", t.id)
dep.set("type", t.depType)
dep.set("split", "PNS")
dep.set("id", "spd_%d" % depSeqId)
depSeqId += 1
for phrase in parse.getiterator("phrase"):
newparse.append(phrase)
# debugging
#print >> sys.stderr, "NEW DEP IN", sId
print >> sys.stderr, "Tokenization missing from", missingTokCount, "sentences"
#indent(root)
if logFile != None:
logFile.close()
# debugging
if output != None:
print >> sys.stderr, "Writing output to", output
ETUtils.write(tree, output)
return tree
def mergeSentences(input, output, verbose=False):
print >> sys.stderr, "Merging sentences into documents"
print >> sys.stderr, "Loading corpus file", input
corpusTree = ETUtils.ETFromObj(input)
corpusRoot = corpusTree.getroot()
counts = defaultdict(int)
for document in corpusRoot.findall("document"):
counts["documents"] += 1
# Check that the entity has only sentence elements as children
children = [x for x in document]
docChildTypes = sorted(set([x.tag for x in children]))
if len(docChildTypes) == 0:
counts["documents-with-no-sentences"] += 1
continue
elif len(docChildTypes) > 1 or docChildTypes[0] != "sentence":
raise Exception("Document '" + str(document.get("id")) +
"' has non-sentence children: " +
str(docChildTypes))
# Process all the child sentence elements
docId = document.get("id")
interactions = []
entities = []
entityById = {}
interactionById = {}
combinedText = ""
calculatedOffset = (0, 0)
for sentence in children:
document.remove(sentence)
sentenceText = sentence.get("head", "") + sentence.get(
"text", "") + sentence.get("tail", "")
sentOffset = sentence.get("charOffset")
if sentence == children[0]:
noDefinedOffsets = sentOffset == None
elif (sentOffset == None) != noDefinedOffsets:
raise Exception("Only some sentences in document '" + docId +
"' have defined offsets")
if sentOffset == None:
if sentence != children[-1]:
sentenceText = sentenceText + " "
calculatedOffset = (calculatedOffset[1],
calculatedOffset[1] + len(sentenceText))
sentOffset = calculatedOffset
else:
sentOffset = Range.charOffsetToSingleTuple(sentOffset)
combinedText += sentenceText
# Collect and update the entity elements
for entity in sentence.findall("entity"):
# Map sentence-level entity offsets to document level
for offsetKey in ("charOffset", "headOffset"):
if entity.get(offsetKey) != None:
offset = Range.charOffsetToTuples(
entity.get(offsetKey))
for i in range(len(offset)):
offset[i] = (offset[i][0] + sentOffset[0],
offset[i][1] + sentOffset[0])
entity.set(offsetKey, Range.tuplesToCharOffset(offset))
# Compare mapped offsets to origOffset, if available
if entity.get("origOffset") != None:
if entity.get("charOffset") != entity.get("origOffset"):
raise Exception(
"Document '" + str(document.get("id")) +
"' entity '" + str(entity.get("id")) +
"' new charOffset differs from origOffset: " +
str([
entity.get("charOffset"),
entity.get("origOffset")
]))
counts["checked-origOffsets"] += 1
del entity.attrib["origOffset"]
assert entity.get("id") not in entityById
entityById[entity.get(
"id"
)] = entity # For re-mapping the interaction 'e1' and 'e2' attributes
entities.append(entity)
counts["moved-entities"] += 1
# Collect and update the interaction elements
for interaction in sentence.findall("interaction"):
assert interaction.get("id") not in interactionById
interactionById[interaction.get(
"id"
)] = interaction # For re-mapping the interaction 'siteOf' attributes
interactions.append(interaction)
counts["moved-interactions"] += 1
# Check that the combined sentence text matches the document text, if available
if document.get("text") != None and document.get(
"text") != combinedText:
if combinedText == document.get(
"text")[0:len(combinedText)] and document.get(
"text")[len(combinedText):].strip() == "":
if verbose:
print >> sys.stderr, "Warning, document '" + document.get(
"id"
) + "' text has trailing whitespace not included in the combined sentence text"
combinedText = document.get("text")
counts["missing-trailing-whitespace"] += 1
else:
raise Exception(
"Document '" + str(document.get("id")) +
"' text differs from combined sentence text: " +
str([document.get("text"), combinedText]))
counts["checked-document-texts"] += 1
# Check that the entities' texts match the document text
for entity in entities:
offset = Range.charOffsetToTuples(entity.get("charOffset"))
if len(offset) == 1: # Compare only continous entities
if not Range.contains((0, len(combinedText)), offset[0]):
raise Exception(
"Document '" + str(document.get("id")) + "' entity '" +
str(entity.get("id")) +
"' offset is not contained in combined sentence text: "
+ str([
entity.attrib, offset, [0, len(combinedText)],
combinedText
]))
combTextSpan = combinedText[offset[0][0]:offset[0][1]]
if entity.get("text") != combTextSpan:
raise Exception(
"Document '" + str(document.get("id")) + "' entity '" +
str(entity.get("id")) +
"' text does not match combined sentence text: " +
str([entity.get("text"), combTextSpan]))
counts["checked-charOffsets"] += 1
# Set the combined text as the document text
document.set("text", combinedText)
# Update entity and interaction ids (not done earlier so that possible error messages will refer to original ids, also because of siteOf-remapping)
for i in range(len(entities)):
entities[i].set("id", docId + ".e" +
str(i)) # Update the id for the document level
for i in range(len(interactions)):
interaction.set("id", docId + ".i" +
str(i)) # Update the id for the document level
# Update interaction e1 and e2 ids (cannot be done earlier because interactions may refer to entities from multiple sentences)
for i in range(len(interactions)):
interaction = interactions[i]
for entKey in ("e1", "e2"):
interaction.set(entKey,
entityById[interaction.get(entKey)].get("id"))
if interaction.get("siteOf") != None:
interaction.set(
"siteOf",
interactionById[interaction.get("siteOf")].get("id"))
# Add the entity and interaction elements to the document
document.extend(entities)
document.extend(interactions)
print >> sys.stderr, "Counts:", dict(counts)
if output != None:
print >> sys.stderr, "Writing output to", output
ETUtils.write(corpusRoot, output)
return corpusTree
def run(input,
output=None,
elementName="entity",
processElement="document",
splitNewlines=False,
debug=False,
bannerPath=None,
trovePath=None):
print >> sys.stderr, "Loading corpus", input
corpusTree = ETUtils.ETFromObj(input)
print >> sys.stderr, "Corpus file loaded"
corpusRoot = corpusTree.getroot()
# Write text to input file
workdir = tempfile.mkdtemp()
if debug:
print >> sys.stderr, "BANNER work directory at", workdir
infile = codecs.open(os.path.join(workdir, "input.txt"), "wt", "utf-8")
idCount = 0
for sentence in corpusRoot.getiterator(processElement):
infile.write(
"U" + str(idCount) + " " +
sentence.get("text").replace("\n", " ").replace("\n", " ") + "\n")
idCount += 1
infile.close()
# Define classpath for java
if bannerPath == None:
bannerPath = Settings.BANNER_DIR
libPath = "/lib/"
# if not os.path.exists(bannerPath + libPath):
# libPath = "/libs/"
# assert os.path.exists(bannerPath + libPath)
assert os.path.exists(bannerPath + libPath + "banner.jar"), bannerPath
oldVersion = True
classPath = bannerPath + "/bin"
for filename in os.listdir(bannerPath + libPath):
#if filename.endswith(".jar"):
# classPath += ":" + bannerPath + libPath + filename
if filename == "uima":
oldVersion = False
classPath += ":" + bannerPath + libPath + "*"
# classPath += ":" + bannerPath + libPath + "banner.jar"
# classPath += ":" + bannerPath + libPath + "dragontool.jar"
# classPath += ":" + bannerPath + libPath + "heptag.jar"
# classPath += ":" + bannerPath + libPath + "commons-collections-3.2.1.jar"
# classPath += ":" + bannerPath + libPath + "commons-configuration-1.6.jar"
# classPath += ":" + bannerPath + libPath + "commons-lang-2.4.jar"
# classPath += ":" + bannerPath + libPath + "mallet.jar"
# classPath += ":" + bannerPath + libPath + "commons-logging-1.1.1.jar"
if oldVersion:
if trovePath == None:
trovePath = Settings.JAVA_TROVE_PATH
assert os.path.exists(trovePath), trovePath
classPath += ":" + trovePath # ":/usr/share/java/trove.jar"
print >> sys.stderr, "Trove library at", trovePath
config = makeConfigXML(workdir, bannerPath, oldVersion)
# Run parser
print >> sys.stderr, "Running BANNER", bannerPath
cwd = os.getcwd()
os.chdir(bannerPath)
if oldVersion: # old version
args = Settings.JAVA.split() + [
"-cp", classPath, "banner.eval.TestModel", config
]
else:
args = Settings.JAVA.split() + [
"-cp", classPath, "banner.eval.BANNER", "test", config
]
print >> sys.stderr, "BANNER command:", " ".join(args)
startTime = time.time()
exitCode = subprocess.call(args)
assert exitCode == 0, exitCode
print >> sys.stderr, "BANNER time:", str(
datetime.timedelta(seconds=time.time() - startTime))
os.chdir(cwd)
# Put sentences in dictionary
sDict = {}
sentenceHasEntities = {}
sCount = 0
for sentence in corpusRoot.getiterator(processElement):
sDict["U" + str(sCount)] = sentence
sentenceHasEntities["U" + str(sCount)] = False
sCount += 1
sentencesWithEntities = 0
totalEntities = 0
nonSplitCount = 0
splitEventCount = 0
# TODO: mention.txt appears to contain predicted entities directly
# To be able to feed BANNER documents (or poorly chopped sentences)
# one should probably remove newlines, as BANNER separates its input
# on newlines. Replacing all \r and \n characters should preserve the
# character offsets.
# Read BANNER results
print >> sys.stderr, "Inserting entities"
if oldVersion:
outfile = codecs.open(os.path.join(workdir, "output.txt"), "rt",
"utf-8")
idfile = codecs.open(os.path.join(workdir, "ids.txt"), "rt", "utf-8")
# Add output to sentences
for line in outfile:
bannerId = idfile.readline().strip()
sentence = sDict[bannerId]
# Find or create container elements
sentenceId = sentence.get("id")
sText = sentence.get("text")
start = 0
entityCount = 0
beginOffset = None
# Add tokens
splits = line.strip().split()
for split in splits:
tokenText, tag = split.rsplit("|", 1)
# Determine offsets by aligning BANNER-generated tokens to original text
cStart = sText.find(tokenText, start)
assert cStart != -1, (tokenText, tag, sText, line)
cEnd = cStart + len(tokenText) - 1
start = cStart + len(tokenText)
if tag == "O":
if beginOffset != None:
## Make element
#ent = ET.Element(elementName)
#ent.set("id", sentenceId + ".e" + str(entityCount))
#ent.set("charOffset", str(beginOffset) + "-" + str(prevEnd))
#ent.set("type", "Protein")
#ent.set("given", "True")
#ent.set("source", "BANNER")
#ent.set("text", sText[beginOffset:prevEnd+1])
entities = makeEntityElements(beginOffset, prevEnd,
sText, splitNewlines,
elementName)
assert len(entities) > 0
nonSplitCount += 1
if len(entities) > 1:
splitEventCount += 1
for ent in entities:
ent.set("id", sentenceId + ".e" + str(entityCount))
sentence.append(ent)
if not sentenceHasEntities[bannerId]:
sentencesWithEntities += 1
sentenceHasEntities[bannerId] = True
totalEntities += 1
entityCount += 1
beginOffset = None
else:
if beginOffset == None:
beginOffset = cStart
prevEnd = cEnd
outfile.close()
idfile.close()
else:
sentenceEntityCount = {}
mentionfile = codecs.open(os.path.join(workdir, "mention.txt"), "rt",
"utf-8")
for line in mentionfile:
bannerId, offsets, word = line.strip().split("|", 2)
offsets = offsets.split()
sentence = sDict[bannerId]
map = getWhiteSpaceLessStringMap(sentence.get("text"))
offsets[0], offsets[1] = fixWhiteSpaceLessOffset(
word, sentence.get("text"), int(offsets[0]), int(offsets[1]),
map)
#offsets[0], offsets[1] = fixStrangeOffset(line.strip(), word, int(offsets[0]), int(offsets[1]), sentence.get("text"))
entities = makeEntityElements(int(offsets[0]), int(offsets[1]),
sentence.get("text"), splitNewlines,
elementName)
entityText = "\n".join([x.get("text") for x in entities])
assert entityText == word, (entityText, word, bannerId, offsets,
sentence.get("id"),
sentence.get("text"))
assert len(entities) > 0, (line.strip(), sentence.get("text"))
nonSplitCount += 1
if len(entities) > 1:
splitEventCount += 1
if bannerId not in sentenceEntityCount:
sentenceEntityCount[bannerId] = 0
for ent in entities:
ent.set(
"id",
sentence.get("id") + ".e" +
str(sentenceEntityCount[bannerId]))
sentence.append(ent)
if not sentenceHasEntities[bannerId]:
sentencesWithEntities += 1
sentenceHasEntities[bannerId] = True
totalEntities += 1
sentenceEntityCount[bannerId] += 1
mentionfile.close()
print >> sys.stderr, "BANNER found", nonSplitCount, "entities in", sentencesWithEntities, processElement + "-elements",
print >> sys.stderr, "(" + str(sCount) + " sentences processed)"
print >> sys.stderr, "New", elementName + "-elements:", totalEntities, "(Split", splitEventCount, "BANNER entities with newlines)"
# Remove work directory
if not debug:
shutil.rmtree(workdir)
else:
print >> sys.stderr, "BANNER working directory for debugging at", workdir
if output != None:
print >> sys.stderr, "Writing output to", output
ETUtils.write(corpusRoot, output)
return corpusTree
