def test_stanford_ner():
# From Wikipedia front page, 10 Feb 2014.
phrase = ("Academy Award-winning actor Philip Seymour Hoffman"
" dies at the age of 46.")
ne = stanford_ner_tag(phrase)
for token, tag in ne:
assert_true(isinstance(token, basestring))
assert_true(tag in ["O", "PERSON"])
names = stanford_ner_tag(phrase, output="names")
# Stanford doesn't pick up "Academy Award". This is not our fault.
# (XXX divise a better test.)
assert_equal(names, [("Philip Seymour Hoffman", "PERSON")])
def test_stanford_ner():
# From Wikipedia front page, 29 March 2016
phrase = ("Benjamin Franklin Tilley was an officer in the United States"
" Navy and the first acting governor of of what is now"
" American Samoa")
ne = stanford_ner_tag(phrase)
for token, tag in ne:
assert_true(isinstance(token, basestring))
assert_true(tag in ["O", "PERSON", "ORGANIZATION", "LOCATION"])
names = stanford_ner_tag(phrase, output="names")
assert_in(("Benjamin Franklin Tilley", "PERSON"), names)
assert_in(("United States Navy", "ORGANIZATION"), names)
assert_in(("American Samoa", "LOCATION"), names)
def test_stanford_ner_encoding():
# Shouldn't raise an exception. Actually more a test for fetch/chardet,
# but detected in the context of Stanford NER, so a non-regression test.
stanford_ner_tag('\xe9toile'.decode('latin-1'))
stanford_ner_tag('\xe9toile')
