Duee1.0信息提取句子级数据预处理

import os

import sys

import json

def read_by_lines(path):

result = list()

with open(path, "r", encoding="utf8") as infile:

for line in infile:

result.append(line.strip())

return result

def write_by_lines(path, data):

with open(path, "w", encoding="utf8") as outfile:

outfile.write(d + "\\n") for d in data

def data_process(path, model="trigger", is_predict=False):

def label_data(data, start, l, _type):

for i in range(start, start + l):#从起始索引到结束

suffix = "B-" if i == start else "I-" #前缀

datai = "{}{}".format(suffix, _type)

return data

sentences = \[\]

output = "text_a" if is_predict else "text_a\\tlabel"#文本,标签

with open(path) as f:

for line in f:

d_json = json.loads(line.strip())#每一行

_id = d_json"id"#id

text_a = [

"," if t == " " or t == "\n" or t == "\t" else t

for t in list(d_json"text".lower())

]# 文本

if is_predict:

sentences.append({"text": d_json"text", "id": _id})

output.append('\002'.join(text_a))

else:

if model == "trigger":

labels = "O" * len(text_a)#标签初始化为全部非实体

if len(d_json.get("event_list", \[\])) == 0:

continue

for event in d_json.get("event_list"):

event_type = event"event_type"#事件类型

start = event"trigger_start_index"#触发词起始索引

trigger = event"trigger"#触发池

#为触发词设置labels

labels = label_data(labels, start, len(trigger),

event_type)

output.append("{}\t{}".format('\002'.join(text_a),

'\002'.join(labels)))

elif model == "role":

labels = "O" * len(text_a)#标签

if len(d_json.get("event_list", \[\])) == 0:

continue

for event in d_json.get("event_list"):

for arg in event"arguments":

role_type = arg"role"#论元角色类型

argument = arg"argument"#论元

start = arg"argument_start_index"#论元起始

labels = label_data(labels, start, len(argument),

role_type)

output.append("{}\t{}".format('\002'.join(text_a),

'\002'.join(labels)))

return output

def schema_process(path, model="trigger"):

def label_add(labels, _type):

if "B-{}".format(_type) not in labels:#不在里面就添加

labels.extend("B-{}".format(_type), "I-{}".format(_type))#B-,I-

return labels

labels = \[\]#存放事件类型标签或角色标签

for line in read_by_lines(path):

d_json = json.loads(line.strip())

if model == "trigger":

labels = label_add(labels, d_json"event_type")

elif model == "role":

for role in d_json"role_list":

labels = label_add(labels, role"role")

labels.append("O")

tags = \[\]

for index, label in enumerate(labels):

tags.append("{}\t{}".format(index, label))

return tags

conf_dir = "./conf/DuEE1.0"

schema_path ='./datasets/DuEE_1_0/event_schema.json'

tags_trigger_path = "{}/trigger_tag.dict".format(conf_dir)

tags_role_path = "{}/role_tag.dict".format(conf_dir)

read_by_lines(schema_path)0

!unzip DuEE_1_0.zip -d ./datasets/

tags_trigger = schema_process(schema_path, "trigger")

os.makedirs(conf_dir,exist_ok=True)

write_by_lines(tags_trigger_path, tags_trigger)

tags_role = schema_process(schema_path, "role")

write_by_lines(tags_role_path, tags_role)

data_dir = "./datasets/DuEE1.0"

trigger_save_dir = "{}/trigger".format(data_dir)

role_save_dir = "{}/role".format(data_dir)

if not os.path.exists(trigger_save_dir):

os.makedirs(trigger_save_dir)

if not os.path.exists(role_save_dir):

os.makedirs(role_save_dir)

train_tri = data_process("./datasets/DuEE_1_0/train.json","trigger")

read_by_lines('./datasets/DuEE_1_0/train.json')0

write_by_lines("{}/train.tsv".format(trigger_save_dir), train_tri)

dev_tri = data_process("./datasets/DuEE_1_0/dev.json","trigger")

write_by_lines("{}/dev.tsv".format(trigger_save_dir), dev_tri)

test_tri = data_process("./datasets/DuEE_1_0/test.json", "trigger")

write_by_lines("{}/test.tsv".format(trigger_save_dir), test_tri)

train_role = data_process("./datasets/DuEE_1_0/train.json", "role")

write_by_lines("{}/train.tsv".format(role_save_dir), train_role)

dev_role = data_process("./datasets/DuEE_1_0/dev.json", "role")

write_by_lines("{}/dev.tsv".format(role_save_dir), dev_role)

test_role = data_process("./datasets/DuEE_1_0/test.json", "role")

write_by_lines("{}/test.tsv".format(role_save_dir), test_role)

相关推荐
奥惟斯9 小时前
GEO 上线前,先把这 3 类实体信号地基补上
人工智能·深度学习·机器学习·自然语言处理·数据挖掘·数据分析·知识图谱
隔窗听雨眠17 小时前
大语言模型推理效率的多维优化与部署取舍
人工智能·语言模型·自然语言处理
小豆庄1 天前
本地视频打码:多模型检测、帧间追踪与18种模糊的选型
opencv·目标检测·ai·自然语言处理·音视频·视频
BioTender2 天前
Hugging Face 发布 Carbon-X:一个能够直接理解 DNA 的大语言模型
人工智能·语言模型·自然语言处理
阡陌数智2 天前
通过特化小型语言模型的词表将推理成本降低百分之七十七
人工智能·语言模型·自然语言处理
Solange__2 天前
图片转3D导出GLB、FBX还是OBJ?按5类任务比较材质、动画与可编辑性
人工智能·3d·ai·自然语言处理·编辑器·blender·材质
大模型任我行2 天前
英伟达:双重采样修正强化学习奖励
人工智能·语言模型·自然语言处理·论文笔记
大鹏的NLP博客2 天前
从格赖斯合作原则看大语言模型与提示词工程
人工智能·自然语言处理·大模型·agent
Martina_03212 天前
AI生成的3D模型打印后沿层纹开裂?用6步检查方向、壁厚与填充
图像处理·人工智能·数学建模·3d·自然语言处理·游戏策划·1024程序员节
zx_741484812 天前
BERT自然语言处理框架深度解析:从Transformer到双向预训练模型
自然语言处理·bert·transformer