def _generate_ungrounded_graph(nodes, edges, ungrounded_query_id=1):
'''ungrounded graph'''
return UngroundedGraph(ungrounded_query_id=ungrounded_query_id,
nodes=nodes,
edges=edges,
important_words_list=[],
abstract_question=[],
grounded_linking=[],
grounded_graph_forest=[])
def generate_ungrounded_graph(ungrounded_nodes=None, span_tree_hybrid_dependency_graph=None):
'''
:param ungrounded_nodes:
:param span_tree_hybrid_dependency_graph:
:return: path: ungrounded edges
'''
visit_set.clear()
global ungrounded_edges
ungrounded_edges = []
path.clear()
construct_edges(ungrounded_nodes=ungrounded_nodes, span_tree_hybrid_dependency_graph=span_tree_hybrid_dependency_graph)
ungrounded_graph = UngroundedGraph(
ungrounded_query_id=1, nodes=ungrounded_nodes, edges=ungrounded_edges,
important_words_list=[], abstract_question=[], grounded_linking=[], grounded_graph_forest=[])
return ungrounded_graph
def _run_ner_topic_entities(qid=None, question_normal=None, gold_graph_query=None, gold_answer=None, gold_sparql_query=None,
node_is_gold=False, q_mode='lcquad'):
"""node mention annotation"""
tokens = parsing_utils.create_tokens(question_normal.split(" "))
"""generate nodes"""
if node_is_gold:
ungrounded_nodes = node_recognition.generate_gold_nodes(question_normal=question_normal)
else:
ungrounded_nodes = node_recognition.generate_nodes(question_normal=question_normal, tokens=tokens)
ungrounded_graphs_list = []
main_ungrounded_graph = UngroundedGraph(
ungrounded_query_id=1, nodes=ungrounded_nodes, edges=[],
important_words_list=[], abstract_question=[], grounded_linking=[], grounded_graph_forest=[])
main_ungrounded_graph.set_blag('ir')
""""""
abstract_question_word = parsing_utils.abstract_question_word_generation(tokens=tokens,
ungrounded_nodes=ungrounded_nodes)
sequence_bert_ner_tag_dict = parsing_utils.get_nertag_sequence(ungrounded_nodes=ungrounded_nodes)
main_ungrounded_graph.sequence_ner_tag_dict = str(sequence_bert_ner_tag_dict)
main_ungrounded_graph.abstract_question = str(abstract_question_word)
main_ungrounded_graph.important_words_list = str([])
ungrounded_graphs_list.append(main_ungrounded_graph)
"""question type"""
compositionality_type, q_function = qtype_interface.question_type_interface(question_normal=question_normal, q_mode=q_mode)
"""generate question annotation"""
question_annotation = QuestionAnnotation(qid=qid,
question=question_normal,
question_normal=question_normal,
tokens=tokens,
span_tree=None,
span_tree_hybrid_dependency_graph=None,
main_ungrounded_graph=main_ungrounded_graph,
sequence_ner_tag_dict=sequence_bert_ner_tag_dict,
gold_graph_query=gold_graph_query,
gold_answer=gold_answer,
gold_sparql_query=gold_sparql_query,
compositionality_type=compositionality_type,
q_function=q_function)
"""generate structure"""
structure = question_annotation.convert_to_structure()
structure.set_ungrounded_graph_forest(ungrounded_graph_forest=ungrounded_graphs_list)
return structure
def read_ungrounded_graph(ungrounded_graph_json):
'''
function: read ungrounded_graph data
:param ungrounded_graph_json: ungrounded_graph json file
:return: ungrounded_graph structure list
'''
ungrounded_query_id = ungrounded_graph_json['ungrounded_query_id']
blag = ''
if 'blag' in ungrounded_graph_json.keys():
blag = ungrounded_graph_json['blag']
nodes = []
edges = []
for node_json in ungrounded_graph_json['nodes']:
if 'score' in node_json.keys():
score = node_json['score']
else:
score = 0.0
if 'ordinal' in node_json.keys():
ordinal = node_json['ordinal']
else:
ordinal = "none"
nodes.append(
UngroundedNode(
nid=node_json["nid"],
node_type=node_json["node_type"],
friendly_name=node_json["friendly_name"],
question_node=node_json["question_node"],
function_str=node_json["function"],
score=score,
normalization_value=node_json["normalization_value"],
type_class=node_json['type_class'],
ordinal=ordinal))
for edge_json in ungrounded_graph_json['edges']:
if 'score' in edge_json.keys():
score = edge_json['score']
else:
score = 0.0
edges.append(
UngroundedEdge(start=edge_json["start"],
end=edge_json["end"],
friendly_name=edge_json["friendly_name"],
score=score))
important_words_list = ungrounded_graph_json['important_words_list']
abstract_question = ungrounded_graph_json['abstract_question']
sequence_ner_tag_dict = ungrounded_graph_json['sequence_ner_tag_dict']
grounded_linking = None
if 'grounded_linking' in ungrounded_graph_json.keys():
grounded_linking = ungrounded_graph_json['grounded_linking']
grounded_graph_list = []
if 'grounded_graph_forest' in ungrounded_graph_json.keys():
# add grounded_graph_forest
for grounded_graph_json in ungrounded_graph_json[
'grounded_graph_forest']:
grounded_graph_list.append(
read_grounded_graph(grounded_graph_json))
return UngroundedGraph(ungrounded_query_id=ungrounded_query_id,
blag=blag,
nodes=nodes,
edges=edges,
important_words_list=important_words_list,
abstract_question=abstract_question,
sequence_ner_tag_dict=sequence_ner_tag_dict,
grounded_linking=grounded_linking,
grounded_graph_forest=grounded_graph_list)