train_data='./datasets/duuie'
output_folder='./datasets/duuie_pre'
ignore_datasets=["DUEE", "DUEE_FIN_LITE"]
schema_folder='./datasets/seen_schema'
# 对CCKS2022 竞赛数据进行预处理
import shutil
# shutil.copytree(train_data,output_folder)
import os
life_folder = os.path.join(output_folder, "DUIE_LIFE_SPO")
org_folder = os.path.join(output_folder, "DUIE_ORG_SPO")
print(life_folder,org_folder)
import json
def load_jsonlines_file(filename):
return [json.loads(line) for line in open(filename, encoding="utf8")]
life_train_instances = load_jsonlines_file(f"{life_folder}/train.json")
org_train_instances = load_jsonlines_file(f"{org_folder}/train.json")
for i in range(27695,27698):
print(life_train_instances[i],'|',org_train_instances[i])
class RecordSchema:
def __init__(self, type_list, role_list, type_role_dict):
self.type_list = type_list
self.role_list = role_list
self.type_role_dict = type_role_dict
def __repr__(self) -> str:
repr_list = [f"Type: {self.type_list}\n", f"Role: {self.role_list}\n", f"Map: {self.type_role_dict}"]
return "\n".join(repr_list)
@staticmethod
def get_empty_schema():
return RecordSchema(type_list=list(), role_list=list(), type_role_dict=dict())
@staticmethod
def read_from_file(filename):
lines = open(filename, encoding="utf8").readlines()
type_list = json.loads(lines[0])# 类型
role_list = json.loads(lines[1]) # 角色
type_role_dict = json.loads(lines[2])#类型－角色
return RecordSchema(type_list, role_list, type_role_dict)
def write_to_file(self, filename):
with open(filename, "w", encoding="utf8") as output:
# 用于将Python对象编码（序列化）为JSON格式的字符串。设置ensure_ascii=False参数
# 会告诉json.dumps()函数不要转义非ASCII字符
output.write(json.dumps(self.type_list, ensure_ascii=False) + "\n")
output.write(json.dumps(self.role_list, ensure_ascii=False) + "\n")
output.write(json.dumps(self.type_role_dict, ensure_ascii=False) + "\n")
RecordSchema.read_from_file(f"{life_folder}/record.schema")
life_relation = RecordSchema.read_from_file(f"{life_folder}/record.schema").role_list
org_relation = RecordSchema.read_from_file(f"{org_folder}/record.schema").role_list
from collections import defaultdict
instance_dict = defaultdict(list)
for instance in life_train_instances + org_train_instances:
instance_dict[instance["text"]] += [instance]
a=[i for i in life_train_instances for j in org_train_instances if i['text']==j['text']]
b=[i for i in org_train_instances for j in a if i['text']==j['text']]
for i in range(3):
print(a[i]['relation'],'|',b[i]['relation'])
dict_1={1:2,3:4}
for i in dict_1:#相当于字典的keys()
print(i)
from typing import Tuple, List, Dict
def merge_instance(instance_list):
def all_equal(_x):#判断是否全相同
for __x in _x:
if __x != _x[0]:
return False
return True
def entity_key(_x):
return (tuple(_x["offset"]), _x["type"])
def relation_key(_x):
return (
tuple(_x["type"]),
tuple(_x["args"][0]["offset"]),
_x["args"][0]["type"],
tuple(_x["args"][1]["offset"]),
_x["args"][1]["type"],
)
def event_key(_x):
return (tuple(_x["offset"]), _x["type"])
assert all_equal([x["text"] for x in instance_list])
element_dict = {
"entity": dict(),
"relation": dict(),
"event": dict(),
}
instance_id_list = list()
for x in instance_list:
instance_id_list += [x["id"]]
for entity in x.get("entity", list()):
element_dict["entity"][entity_key(entity)] = entity
for relation in x.get("relation", list()):
element_dict["relation"][relation_key(relation)] = relation
for event in x.get("event", list()):
element_dict["event"][event_key(event)] = event
return {
"id": "-".join(instance_id_list),
"text": instance_list[0]["text"],
"tokens": instance_list[0]["tokens"],
"entity": list(element_dict["entity"].values()),
"relation": list(element_dict["relation"].values()),
"event": list(element_dict["event"].values()),
}
for text in instance_dict:
instance_dict[text] = merge_instance(instance_dict[text])
for i in range(800,802):
print(list(instance_dict.values())[i]['relation'])
import copy
with open(f"{life_folder}/train.json", "w") as output:
for instance in instance_dict.values():
new_instance = copy.deepcopy(instance)
new_instance["relation"] = list(filter(lambda x: x["type"] in life_relation, instance["relation"]))
output.write(json.dumps(new_instance) + "\n")
with open(f"{org_folder}/train.json", "w") as output:
for instance in instance_dict.values():
new_instance = copy.deepcopy(instance)
new_instance["relation"] = list(filter(lambda x: x["type"] in org_relation, instance["relation"]))
output.write(json.dumps(new_instance) + "\n")
a_instances = load_jsonlines_file(f"{life_folder}/train.json")
b_instances = load_jsonlines_file(f"{org_folder}/train.json")
print(len(a_instances),len(b_instances))
import yaml
def load_definition_schema_file(filename):
return yaml.load(open(filename, encoding="utf8"), Loader=yaml.FullLoader)
aa = load_definition_schema_file(os.path.join(schema_folder,'体育竞赛.yaml'))
mm=list()
for i in aa['事件'].values():
mm+=i["参数"]
mm=list(set(mm))
[x for x in aa['事件']]
aa['事件']['退役']["参数"].keys()
aaa={1:2,3:4}
for k,v in aaa.items():
print(k,v)
def dump_schema(output_folder, schema_dict):
if not os.path.exists(output_folder):
os.makedirs(output_folder)
for schema_name, schema in schema_dict.items():
schema_file = f"{output_folder}/{schema_name}.schema"
with open(schema_file, "w", encoding="utf8") as output:
for element in schema:
output.write(json.dumps(element, ensure_ascii=False) + "\n")
def dump_event_schema(event_map, output_folder):
role_list = list()
for roles in event_map.values():
role_list += roles["参数"]
rols_list = list(set(role_list))
type_list = list(event_map.keys())
type_role_map = {event_type: list(event_map[event_type]["参数"].keys()) for event_type in event_map}
dump_schema(
output_folder=output_folder,
schema_dict={
"entity": [[], [], {}],
"relation": [[], [], {}],
"event": [type_list, rols_list, type_role_map],
"record": [type_list, rols_list, type_role_map],
},
)
def filter_event_in_instance(instances,required_event_types):
"""Filter events in the instance, keep event mentions with `required_event_types`
过滤实例中的事件，只保留需要的事件类别的事件标注
"""
new_instances = list()
for instance in instances:
new_instance = copy.deepcopy(instance)
new_instance["event"] = list(filter(lambda x: x["type"] in required_event_types, new_instance["event"]))
new_instances += [new_instance]
return new_instances
def dump_instances(instances, output_filename):
with open(output_filename, "w", encoding="utf8") as output:
for instance in instances:
output.write(json.dumps(instance, ensure_ascii=False) + "\n")
def filter_event(data_folder, event_types, output_folder):
"""Keep event with `event_types` in `data_folder` save to `output_folder`
过滤 `data_folder` 中的事件，只保留 `event_types` 类型事件保存到 `output_folder`"""
dump_event_schema(event_types, output_folder)
for split in ["train", "val"]:
filename = os.path.join(data_folder, f"{split}.json")
instances = [json.loads(line.strip()) for line in open(filename, encoding="utf8")]
new_instances = filter_event_in_instance(instances, required_event_types=event_types)
dump_instances(new_instances, os.path.join(output_folder, f"{split}.json"))
# 对事件数据进行预处理，过滤除 `灾害意外` 和 `体育竞赛` 外的事件标注
for schema in ["灾害意外", "体育竞赛"]:
print(f"Building {schema} dataset ...")
duee_folder = os.path.join(output_folder, "DUEE")
schema_file = os.path.join(schema_folder, f"{schema}.yaml")
output_folder2 = os.path.join(output_folder, schema)
schema = load_definition_schema_file(schema_file)
filter_event(
duee_folder,
schema["事件"],
output_folder2,
)
ty_instances = load_jsonlines_file(f"{output_folder}/体育竞赛/train.json")
zh_instances = load_jsonlines_file(f"{output_folder}/灾害意外/train.json")
print(len(ty_instances),len(zh_instances))
for i in range(11508,11608):
print(ty_instances[i],'|',zh_instances[i])
bb=load_definition_schema_file(os.path.join(schema_folder, "金融信息.yaml"))
for i in bb['事件'].keys():
print(i)
mm=list()
mm+=bb['事件']['中标']["参数"]
mm=list(set(mm))
bb["事件"]['中标']["参数"] .keys()
for schema in ["金融信息"]:
print(f"Building {schema} dataset ...")
duee_fin_folder = os.path.join(output_folder, "DUEE_FIN_LITE")
schema_file = os.path.join(schema_folder, f"{schema}.yaml")
output_folder2 = os.path.join(output_folder, schema)
schema = load_definition_schema_file(schema_file)
# 依据不同事件类别将多事件抽取分割成多个单事件类型抽取
# Separate multi-type extraction to multiple single-type extraction
for event_type in schema["事件"]:
filter_event(
duee_fin_folder,
{event_type: schema["事件"][event_type]},
output_folder2 + "_" + event_type,
)
vv=load_jsonlines_file(f"{output_folder}/DUEE_FIN_LITE/train.json")
zb_instances = load_jsonlines_file(f"{output_folder}/金融信息_中标/train.json")
zy_instances = load_jsonlines_file(f"{output_folder}/金融信息_质押/train.json")
print(len(zb_instances),len(zy_instances))
for i in range(6985,7015):
print(zb_instances[i],'|',zy_instances[i])
def annonote_graph(
entities: List[Dict] = [],
relations: List[Dict] = [],
events: List[Dict] = []):
spot_dict = dict()
asoc_dict = defaultdict(list)
# 将实体关系事件转换为点关联图
def add_spot(spot):
spot_key = (tuple(spot["offset"]), spot["type"])
spot_dict[spot_key] = spot
def add_asoc(spot, asoc, tail):
spot_key = (tuple(spot["offset"]), spot["type"])
asoc_dict[spot_key] += [(tuple(tail["offset"]), tail["text"], asoc)]
for entity in entities:
add_spot(spot=entity)
for relation in relations:
add_spot(spot=relation["args"][0])
add_asoc(spot=relation["args"][0], asoc=relation["type"], tail=relation["args"][1])
for event in events:
add_spot(spot=event)
for argument in event["args"]:
add_asoc(spot=event, asoc=argument["type"], tail=argument)
spot_asoc_instance = list()
for spot_key in sorted(spot_dict.keys()):
offset, label = spot_key
if len(spot_dict[spot_key]["offset"]) == 0:
continue
spot_instance = {
"span": spot_dict[spot_key]["text"],
"label": label,
"asoc": list(),
}
for tail_offset, tail_text, asoc in sorted(asoc_dict.get(spot_key, [])):
if len(tail_offset) == 0:
continue
spot_instance["asoc"] += [(asoc, tail_text)]
spot_asoc_instance += [spot_instance]
spot_labels = set([label for _, label in spot_dict.keys()])
asoc_labels = set()
for _, asoc_list in asoc_dict.items():
for _, _, asoc in asoc_list:
asoc_labels.add(asoc)
return spot_labels, asoc_labels, spot_asoc_instance
def add_spot_asoc_to_single_file(filename):
instances = [json.loads(line) for line in open(filename, encoding="utf8")]
print(f"Add spot asoc to {filename} ...")
with open(filename, "w", encoding="utf8") as output:
for instance in instances:
spots, asocs, spot_asoc_instance = annonote_graph(
entities=instance["entity"],#实体
relations=instance["relation"],#关系
events=instance["event"],#事件
)
# 为对象添加spot_asoc
instance["spot_asoc"] = spot_asoc_instance
# 为对象添加spot
instance["spot"] = list(spots)
# 为对象添加asoc
instance["asoc"] = list(asocs)
output.write(json.dumps(instance, ensure_ascii=False) + "\n")
ff = os.path.join(output_folder,'金融信息_企业破产',"train.json")
ff_instances = [json.loads(line) for line in open(ff, encoding="utf8")]
for i in range(1046,1050):
print(ff_instances[i])
a,b,yyj=annonote_graph( entities=ff_instances[11000]["entity"],
relations=ff_instances[11000]["relation"],
events=ff_instances[11000]["event"],)
data_folder=output_folder
def merge_schema(schema_list: List[RecordSchema]):
type_set = set()
role_set = set()
type_role_dict = defaultdict(list)
for schema in schema_list:
for type_name in schema.type_list:
type_set.add(type_name)
for role_name in schema.role_list:
role_set.add(role_name)
for type_name in schema.type_role_dict:
type_role_dict[type_name] += schema.type_role_dict[type_name]
for type_name in type_role_dict:
type_role_dict[type_name] = list(set(type_role_dict[type_name]))
return RecordSchema(
type_list=list(type_set),
role_list=list(role_set),
type_role_dict=type_role_dict,
)
def convert_duuie_to_spotasoc(data_folder, ignore_datasets):
schema_list = list()
for task_folder in os.listdir(data_folder):#过滤无效
if task_folder in ignore_datasets:
continue
if not os.path.isdir(os.path.join(data_folder, task_folder)):#过滤非文件夹
continue
print(f"Add spot asoc to {task_folder} ...")
# 读取单任务的 Schema
task_schema_file = os.path.join(data_folder, task_folder, "record.schema")
# 向单任务数据中添加 Spot Asoc 标注
add_spot_asoc_to_single_file(os.path.join(data_folder, task_folder, "train.json"))
add_spot_asoc_to_single_file(os.path.join(data_folder, task_folder, "val.json"))
record_schema = RecordSchema.read_from_file(task_schema_file)
schema_list += [record_schema]
# 融合不同任务的 Schema
multi_schema = merge_schema(schema_list)
multi_schema.write_to_file(os.path.join(data_folder, "record.schema"))
convert_duuie_to_spotasoc(output_folder,ignore_datasets)
分布式存储)
