class LexicalNormalizer(object):
cname = __name__ + '.LexicalNormalizer'
def __init__(self,vocabulary_set):
# instance oov detector
self.detector = IVTermDetectorBasicFactory.create_detector(vocabulary_set)
# instance analyzers and push them to the candidate generator
cand_generator = CandidateGenerator()
for resource in vocabulary_set:
cand_generator.add_analyzer("metric-space", MetricSpaceAnalyzerFactory.create_analyzer(resource))
self.cand_generator = cand_generator
# instance preprocessor and logger
self.preprocessor = BasicDocumentPreprocessor()
self.logger = logging.getLogger(LexicalNormalizer.cname)
def _generate_alternatives(self, doc_items):
norm_items = []
for item in doc_items:
if self.detector.detect_term(item):
norm_items.append(item)
else:
norm_items.append(self.cand_generator.generate_candidates(item))
return norm_items
def normalize_document(self, doc):
proc_document = self.preprocessor.process(doc)
item_alternatives = self._generate_alternatives(proc_document)
return item_alternatives
def __init__(self,vocabulary_set):
# instance oov detector
self.detector = IVTermDetectorBasicFactory.create_detector(vocabulary_set)
# instance analyzers and push them to the candidate generator
cand_generator = CandidateGenerator()
for resource in vocabulary_set:
cand_generator.add_analyzer("metric-space", MetricSpaceAnalyzerFactory.create_analyzer(resource))
self.cand_generator = cand_generator
# instance preprocessor and logger
self.preprocessor = BasicDocumentPreprocessor()
self.logger = logging.getLogger(LexicalNormalizer.cname)
