def test_correct_tree_1(self):
checker = SyntaxChecker(GRAMMAR_FILE_PATH)
tree_dict = {
'root': {
'type':
'ROOT',
'children': [{
'type':
'COUNT',
'children': [{
'type':
'PROPERTY',
'children': [{
'type':
'ENTITY',
'children': [{
'type': 'TOKEN',
'id': 1
}, {
'type': 'TOKEN',
'id': 2
}]
}]
}]
}]
},
'tokens': ['a', 'b', 'c', 'd', 'e', 'f']
}
query_tree = QueryTree.from_dict(tree_dict)
self.assertTrue(checker.validate(query_tree))
class QuestionParser(object):
'''
Parses the input query text and returns a set of candidate constituency trees
'''
def __call__(self, query_text: str) -> List[dict]:
tokens = run_task(Task.TOKENIZE, query_text)
self.syntax_validator = SyntaxChecker(GRAMMAR_FILE_PATH)
self.__prepare_input(tokens)
self.__run_ncrfpp()
candidates = self.__decode_labels(tokens)
# Statistically parsed trees might not validate the grammar. Discard invalid trees
candidates = list(
filter(lambda tree: self.syntax_validator.validate(tree),
candidates))
candidates = list(
map(
lambda tree: tree.to_serializable(
SerializationFormat.HIERARCHICAL_DICT), candidates))
#print('Produced {}/{} valid candidates!'.format(len(candidates), TREE_CANDIDATES_N_BEST))
return candidates
def __prepare_input(self, tokens):
'''
Create an input file for the NCRFPP library. It contains the query to be tagged.
'''
with open(INTERMEDIATE_QUERY_FILE_PATH,
'w') as intermediate_output_file:
intermediate_output_file.write('-BOS-\t-BOS-\t-BOS-\n')
for token in tokens:
intermediate_output_file.write(token + '\tTOKEN\tUNUSED\n')
intermediate_output_file.write('-EOS-\t-EOS-\t-EOS-\n')
def __run_ncrfpp(self):
'''
Run the NCRFPP algorithm on the input files
'''
# Write input of ncrfpp in a configuration file
config_string = "### Decode ###\nstatus=decode\n"
config_string += "raw_dir=" + INTERMEDIATE_QUERY_FILE_PATH + "\n"
config_string += "decode_dir=" + MODEL_FILE_PATH + ".output" + "\n"
config_string += "dset_dir=" + MODEL_FILE_PATH + ".dset" + "\n"
config_string += "load_model_dir=" + MODEL_FILE_PATH + ".model" + "\n"
config_string += "gpu=False\n"
config_string += "nbest=" + str(TREE_CANDIDATES_N_BEST) + "\n"
with open(NCRFPP_DECODE_CONFIG_FILE_PATH, "w",
encoding="utf-8") as decode_config_file:
decode_config_file.write(config_string)
ncrfpp_command_string = "python " + NCRFPP_REPOSITORY_PATH + "/main.py --config " + NCRFPP_DECODE_CONFIG_FILE_PATH
os.system(ncrfpp_command_string)
def __decode_labels(self, tokens):
'''
Process the output of the NCRFPP algorithm.
'''
parenthesized_trees = []
with open(MODEL_FILE_PATH + ".output",
encoding="utf-8") as ncrfpp_output_file:
lines = ncrfpp_output_file.readlines()
lines = [line.strip().split(' ') for line in lines[1:]]
for prediction_index in range(0, TREE_CANDIDATES_N_BEST):
try:
sentence = []
pred = []
for token_index, line_tokens in enumerate(lines):
# The tree2labels algorithm is not aware of 'UNUSED' tokens. We first have to eliminate them, so the sequence_to_parenthesis algorithm
# reconstructs the tree corectly
if len(line_tokens
) >= 2 and 'UNUSED' != line_tokens[1]:
sentence.append(
(str(token_index - 1),
'TOKEN')) # -1 because of the -BOS- line
pred.append(line_tokens[1 + prediction_index])
# The main decoding process of tree2labels repo
parenthesized_trees.extend(
sequence_to_parenthesis([sentence], [pred]))
except:
print("Error decoding tree")
candidates = []
for tree in parenthesized_trees:
def tree2dict(tree: Tree):
result = {}
result['type'] = tree.label()
children = [
tree2dict(t) if isinstance(t, Tree) else t for t in tree
]
if tree.label() == 'TOKEN':
result['id'] = int(children[0])
elif children:
result['children'] = children
return result
tree = tree.strip()
nltk_tree: Tree = Tree.fromstring(tree,
remove_empty_top_bracketing=True)
root = tree2dict(nltk_tree)
dict_tree = {'root': root, 'tokens': tokens}
# If first tokens are 'who', 'where', 'when' or 'how many" add it as a type to the answer node.
try:
if tokens[0].lower() in {'who', 'where', 'when'}:
type_node = {
'type': NodeType.TYPE.value,
'children': [{
'type': NodeType.TOKEN.value,
'id': 0
}]
}
root['children'][0]['children'].append(type_node)
if (tokens[0].lower(), tokens[1].lower()) == (
'how', 'many'
) and root['children'][0]['type'] != NodeType.COUNT.value:
type_node = {
'type':
NodeType.TYPE.value,
'children': [{
'type': NodeType.TOKEN.value,
'id': 0
}, {
'type': NodeType.TOKEN.value,
'id': 1
}]
}
root['children'][0]['children'].append(type_node)
except:
pass # Tree is probabily invalid
query_tree = QueryTree.from_dict(dict_tree)
candidates.append(query_tree)
return candidates