def test_dependency_parsing(self):
predictor = pretrained.biaffine_parser_stanford_dependencies_todzat_2017()
sentence = """He ate spaghetti with chopsticks."""
result = predictor.predict_json({"sentence": sentence})
# Note that this tree is incorrect. We are checking here that the decoded
# tree is _actually a tree_ - in greedy decoding versions of the dependency
# parser, this sentence has multiple heads. This test shouldn't really live here,
# but it's very difficult to re-create a concrete example of this behaviour without
# a trained dependency parser.
assert result['words'] == ['He', 'ate', 'spaghetti', 'with', 'chopsticks', '.']
assert result['pos'] == ['PRP', 'VBD', 'NNS', 'IN', 'NNS', '.']
assert result['predicted_dependencies'] == ['nsubj', 'root', 'dobj', 'prep', 'pobj', 'punct']
assert result['predicted_heads'] == [2, 0, 2, 2, 4, 2]
def main(args):
if args.tagger == "srl":
model = srl_with_elmo_luheng_2018()
tagger_function = model.predict
elif args.tagger == "oie":
model = open_information_extraction_stanovsky_2018()
tagger_function = lambda sentence: model.predict_json(
{"sentence": sentence})
elif args.tagger == "dep":
model = biaffine_parser_stanford_dependencies_todzat_2017()
tagger_function = lambda sentence: get_verb_info_from_graph(
get_nx_graph_from_dep(model.predict(sentence)))
else:
raise RuntimeError(f"Unknown tagger type: {args.tagger}")
if args.include_coref:
coref_model = neural_coreference_resolution_lee_2017()
else:
coref_model = None
if args.verbose:
logging.basicConfig(level=logging.INFO)
make_files_for_semparse(args.data_files_path, args.output_path,
tagger_function, coref_model)
def test_dependency_parsing(self):
predictor = pretrained.biaffine_parser_stanford_dependencies_todzat_2017(
)
sentence = """He ate spaghetti with chopsticks."""
result = predictor.predict_json({"sentence": sentence})
# Note that this tree is incorrect. We are checking here that the decoded
# tree is _actually a tree_ - in greedy decoding versions of the dependency
# parser, this sentence has multiple heads. This test shouldn't really live here,
# but it's very difficult to re-create a concrete example of this behaviour without
# a trained dependency parser.
assert result["words"] == [
"He", "ate", "spaghetti", "with", "chopsticks", "."
]
assert result["pos"] == ["PRP", "VBD", "NNS", "IN", "NNS", "."]
assert result["predicted_dependencies"] == [
"nsubj",
"root",
"dobj",
"prep",
"pobj",
"punct",
]
assert result["predicted_heads"] == [2, 0, 2, 2, 4, 2]