def __process_term(self, terms, node):
"""Adds the term to the terms table."""
term = node[0]
pos = node[1]
pos_key = pos[0].lower()
# continue only if we have a noun or a verb
if not pos_key in ('n', 'v'):
return
term = self._lemmatizer.lemmatize(term, pos_key)
if term is None:
return
term = normalize_feature_name(term)
if term in self._stopwords:
return
key = make_feature_key(term)
if key not in terms:
terms[key] = 0
terms[key] += 1
raise ProcessingException(str(e))
self._cache.put(resource.uri, result)
else:
self._logger.debug('Read from cache.')
# extracted entities
entities = {}
# parse the XML data
tree = parse_xml(result)
entity_ets = tree.xpath('//entity')
if len(entity_ets) < self._min_entity_count:
raise ProcessingException('Insufficient entities')
for entity_et in entity_ets:
type = entity_et.find('type').text.lower()
name = entity_et.find('text').text
score = float(entity_et.find('relevance').text)
disambiguated_et = entity_et.find('disambiguated')
if disambiguated_et is not None:
name = disambiguated_et.find('name').text
name = normalize_feature_name(name)
key = make_feature_key((type, name))
entities[key] = score
context[ProcessingContext.EXTRACTED_ENTITIES] = entities
