def __init__(self, pos, chunk, event, classify, mallet_memory='256m'):
self.clear_line_counter()
self.posTagger = pos_tagger_stdin.PosTagger() if pos else None
self.chunkTagger = chunk_tagger_stdin.ChunkTagger() if chunk and pos else None
self.eventTagger = event_tagger_stdin.EventTagger() if event and pos else None
self.llda = GetLLda() if classify else None
if pos and chunk:
self.ner_model = 'ner.model'
elif pos:
self.ner_model = 'ner_nochunk.model'
else:
self.ner_model = 'ner_nopos_nochunk.model'
self.ner = GetNer(self.ner_model, memory=mallet_memory)
self.fe = Features.FeatureExtractor('%s/data/dictionaries' % (BASE_DIR))
self.capClassifier = cap_classifier.CapClassifier()
self.vocab = Vocab('%s/hbc/data/vocab' % (BASE_DIR))
self.dictMap = {}
self.dictMap = self.dictMap
i = 1
for line in open('%s/hbc/data/dictionaries' % (BASE_DIR)):
dictionary = line.rstrip('\n')
self.dictMap[i] = dictionary
i += 1
self.dict2index = {}
for i in self.dictMap.keys():
self.dict2index[self.dictMap[i]] = i
if self.llda:
self.dictionaries = Dictionaries('%s/data/LabeledLDA_dictionaries3' % (BASE_DIR), self.dict2index)
self.entityMap = {}
i = 0
if self.llda:
for line in open('%s/hbc/data/entities' % (BASE_DIR)):
entity = line.rstrip('\n')
self.entityMap[entity] = i
i += 1
self.dict2label = {}
for line in open('%s/hbc/data/dict-label3' % (BASE_DIR)):
(dictionary, label) = line.rstrip('\n').split(' ')
self.dict2label[dictionary] = label
#return subprocess.Popen('java -Xmx256m -cp %s/mallet-2.0.6/lib/mallet-deps.jar:%s/mallet-2.0.6/class cc.mallet.fst.SimpleTaggerStdin --weights sparse --model-file %s/models/ner/%s' % (BASE_DIR, BASE_DIR, BASE_DIR, ner_model),
return subprocess.Popen('java -Xmx512m -cp %s/mallet-2.0.6/lib/mallet-deps.jar:%s/mallet-2.0.6/class cc.mallet.fst.SimpleTaggerStdin --weights sparse --model-file %s/models/ner/%s' % (BASE_DIR, BASE_DIR, BASE_DIR, ner_model), shell=True, close_fds=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
def GetLLda():
return subprocess.Popen('%s/hbc/models/LabeledLDA_infer_stdin.out %s/hbc/data/combined.docs.hbc %s/hbc/data/combined.z.hbc 100 100' % (BASE_DIR, BASE_DIR, BASE_DIR), shell=True, close_fds=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
def recomputeScores():
for item in scores:
computedScores[item] = (scores[item]+ 1)/((scores[item] + len(scores))*1.0)
def displayScores():
for item in computedScores:
print(str(item) + " : " + str(computedScores[item]))
posTagger = pos_tagger_stdin.PosTagger()
chunkTagger = chunk_tagger_stdin.ChunkTagger()
eventTagger = event_tagger_stdin.EventTagger()
llda = GetLLda()
ner_model = 'ner.model'
ner = GetNer(ner_model)
fe = Features.FeatureExtractor('%s/data/dictionaries' % (BASE_DIR))
capClassifier = cap_classifier.CapClassifier()
vocab = Vocab('%s/hbc/data/vocab' % (BASE_DIR))
dictMap = {}
i = 1
