def get_patterns_from_dict(event_elements):
"""
将提取出的事件要素转换成特征
:param event_elements: 字典形式的事件要素
:return patterns: 字典形式的特征
"""
patterns = dict()
# 从事件要素中的"加刑因素"提取出三个特征：01死亡人数、02重伤人数、03轻伤人数
patterns["01死亡人数"], patterns["02重伤人数"], patterns["03轻伤人数"] = extract_seg(
"".join(event_elements["加刑因素"]))
# 从事件要素中的"主次责任"提取出特征：04责任认定
patterns["04责任认定"] = find_element(event_elements["主次责任"], "全部责任")
# 从事件要素中的"加刑因素"提取出8个特征
patterns["05是否酒后驾驶"] = find_element(event_elements["加刑因素"], "酒")
patterns["06是否吸毒后驾驶"] = find_element(event_elements["加刑因素"], "毒")
patterns["07是否无证驾驶"] = find_element(event_elements["加刑因素"], "驾驶证", "证")
patterns["08是否无牌驾驶"] = find_element(event_elements["加刑因素"], "牌照", "牌")
patterns["09是否不安全驾驶"] = find_element(event_elements["加刑因素"], "安全")
patterns["10是否超载"] = find_element(event_elements["加刑因素"], "超载")
patterns["11是否逃逸"] = find_element(event_elements["加刑因素"], "逃逸", "逃离")
patterns["是否初犯偶犯"] = 1 - int(find_element(event_elements["加刑因素"], "前科"))
# 从事件要素中的"减刑因素"提取出7个特征
patterns["12是否抢救伤者"] = find_element(event_elements["减刑因素"], "抢救", "施救")
patterns["13是否报警"] = find_element(event_elements["减刑因素"], "报警", "自首", "投案")
patterns["14是否现场等待"] = find_element(event_elements["减刑因素"], "现场", "等候")
patterns["15是否赔偿"] = find_element(event_elements["减刑因素"], "赔偿")
patterns["16是否认罪"] = find_element(event_elements["减刑因素"], "认罪")
patterns["17是否如实供述"] = find_element(event_elements["减刑因素"], "如实")
if patterns["是否初犯偶犯"] == 0:
patterns["18是否初犯偶犯"] = "0"
else:
patterns["18是否初犯偶犯"] = "1"
return patterns
"/home/zhangshiwei/Event-Extraction/01数据预处理/preprocessed_data.txt",
is_label=True)
num_cases = len(
glob.glob(
"/home/zhangshiwei/Event-Extraction/06判决结果预测/特征提取/data/单个案件/*.txt"))
f1 = open("/home/zhangshiwei/Event-Extraction/01数据预处理/preprocessed_data.txt",
"r",
encoding="utf-8")
contents = f1.readlines()
for i in range(1, num_cases + 1):
file_name = "data/单个案件/" + str(i) + ".txt"
result = get_event_elements(file_name)
patterns = get_patterns_from_dict(result)
# 因为目前CRF提取的效果还不够好，伤亡情况可能没有提取出来
# 保险起见，对整个案件进行提取死亡人数等3个特征，而非事件抽取的结果
patterns['01死亡人数'], patterns['02重伤人数'], patterns['03轻伤人数'] = extract_seg(
contents[i - 1])
patterns["判决结果"] = labels[i - 1]
del patterns["是否初犯偶犯"]
del patterns["03轻伤人数"]
del patterns["17是否如实供述"]
rows.append(patterns)
f1.close()
# 写回数据
with open("data.csv", "w", newline='') as f:
f_csv = csv.DictWriter(f, headers)
f_csv.writeheader()
f_csv.writerows(rows)
]
rows = []
# 提取标签
labels = label_case("preprocessed_data.txt", is_label=True)
num_cases = 1
f1 = open("preprocessed_data.txt", "r", encoding="utf-8")
cases = f1.readlines()
event_elements = get_event_elements("CRF结果.txt")
patterns = get_patterns_from_dict(event_elements)
# 因为目前CRF提取的效果还不够好，伤亡情况可能没有提取出来
# 保险起见，对整个案件进行提取死亡人数等3个特征，而非事件抽取的结果
patterns['01死亡人数'], patterns['02重伤人数'], patterns['03轻伤人数'] = extract_seg(line2)
patterns["判决结果"] = labels[0]
del patterns["是否初犯偶犯"]
del patterns["03轻伤人数"]
del patterns["17是否如实供述"]
rows.append(patterns)
f1.close()
# 写回数据
with open("pattern.csv", "w", newline='') as f:
f_csv = csv.DictWriter(f, headers)
# f_csv.writeheader()
f_csv.writerows(rows)
f3 = open("pattern.csv", "r")
f3_csv = list(csv.reader(f3))
num_cases = len(
glob.glob(
"/home/zhangshiwei/Event-Extraction/06判决结果预测/特征提取/data/单个案件/*.txt"))
f1 = open("/home/zhangshiwei/Event-Extraction/01数据预处理/preprocessed_data.txt",
"r",
encoding="utf-8")
cases = f1.readlines()
for i in range(1, num_cases + 1):
file_name = "data/单个案件/" + str(i) + ".txt"
event_elements = get_event_elements(file_name)
patterns = get_patterns_from_dict(event_elements)
# 因为目前CRF提取的效果还不够好，伤亡情况可能没有提取出来
# 保险起见，对整个案件进行提取死亡人数等3个特征，而非事件抽取的结果
patterns['01死亡人数'], patterns['02重伤人数'], patterns['03轻伤人数'] = extract_seg(
cases[i - 1])
patterns["判决结果"] = labels[i - 1]
del patterns["是否初犯偶犯"]
del patterns["03轻伤人数"]
del patterns["17是否如实供述"]
rows.append(patterns)
f1.close()
# 写回数据
with open("data.csv", "w", newline='') as f:
f_csv = csv.DictWriter(f, headers)
# f_csv.writeheader()
f_csv.writerows(rows)