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import os
import json
import argparse
import re
from copy import deepcopy
from tqdm import tqdm
from utils import find_head, WhitespaceTokenizer, find_arg_span
import spacy
print("convert_gen_to_output2.py")
def extract_args_from_template(ex, template, ontology_dict,):
# extract argument text
template_words = template[0].strip().split()
predicted_words = ex['predicted'].strip().split()
predicted_args = {}
t_ptr= 0
p_ptr= 0
evt_type = get_event_type(ex)[0]
while t_ptr < len(template_words) and p_ptr < len(predicted_words):
if re.match(r'<(arg\d+)>', template_words[t_ptr]):
m = re.match(r'<(arg\d+)>', template_words[t_ptr])
arg_num = m.group(1)
arg_name = ontology_dict[evt_type.replace('n/a','unspecified')][arg_num]
if predicted_words[p_ptr] == '<arg>':
# missing argument
p_ptr +=1
t_ptr +=1
else:
arg_start = p_ptr
while (p_ptr < len(predicted_words)) and (predicted_words[p_ptr] != template_words[t_ptr+1]):
p_ptr+=1
arg_text = predicted_words[arg_start:p_ptr]
predicted_args[arg_name] = arg_text
t_ptr+=1
# aligned
else:
t_ptr+=1
p_ptr+=1
return predicted_args
def get_event_type(ex):
evt_type = []
for evt in ex['evt_triggers']:
for t in evt[2]:
evt_type.append( t[0])
return evt_type
def check_coref(ex, arg_span, gold_spans):
for clus in ex['corefs']:
if arg_span in clus:
matched_gold_spans = [span for span in gold_spans if span in clus]
if len(matched_gold_spans) > 0:
return matched_gold_spans[0]
return arg_span
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gen-file',type=str, default='checkpoints/gen-new-tokenization-pred/sample_predictions.jsonl')
parser.add_argument('--test-file', type=str,default='data/RAMS_1.0/data/test_head.jsonlines')
parser.add_argument('--output-file',type=str, default='test_output.jsonl')
parser.add_argument('--ontology-file',type=str, default='aida_ontology_new.csv')
parser.add_argument('--head-only',action='store_true',default=False)
parser.add_argument('--coref', action='store_true', default=False)
args = parser.parse_args()
nlp = spacy.load('en_core_web_sm')
nlp.tokenizer = WhitespaceTokenizer(nlp.vocab)
# read ontology 读取事件本体 模板文件中的内容
ontology_dict ={}
with open('aida_ontology_new.csv','r') as f:
for lidx, line in enumerate(f):
# 跳过第一行表头字段
if lidx == 0:# header
continue
fields = line.strip().split(',')
# 说明该事件类型下不存在待抽取的论元
if len(fields) < 2:
break
# 事件类型是第一个
evt_type = fields[0]
# 从第三个元素往后都是待抽取论语及其论元角色
arguments = fields[2:]
# 获取该事件类型下带带抽取的论元数量
args_len = 0
for i, arg in enumerate(arguments):
if arg != '':
args_len = args_len + 1
# 将事件本体字典中添加事件类型的key，该key下对应的value为模板
# 利用args_len将template中的子模板数量进行循环增加， 将后续的子模板通过字符串拼接的方式进行增加
# 最终的模板样式变为 what is the <arg1> in <trg> what is the <arg2> in <trg>
# 先利用一个临时的字符串变量来存储模板 ----------> temp_template
temp_template = []
for i in range(len(arguments)):
temp_template.append(" what is the <arg{}> in <trg>".format(i + 1))
print(temp_template)
# 在事件本体字典中建立key-value 以事件类型为关键字
ontology_dict[evt_type] = {
'template': temp_template
}
for i, arg in enumerate(arguments):
if arg !='':
ontology_dict[evt_type]['arg{}'.format(i+1)] = arg
ontology_dict[evt_type][arg] = 'arg{}'.format(i+1)
examples = {}
print(args.gen_file)
# data/RAMS_1.0/data/test_head_coref.jsonlines
with open(args.test_file, 'r') as f:
for line in f:
ex = json.loads(line.strip())
ex['ref_evt_links'] = deepcopy(ex['gold_evt_links'])
ex['gold_evt_links'] = []
examples[ex['doc_key']] =ex
# checkpoints/gen-RAMS-pred/predictions.jsonl
with open(args.gen_file,'r') as f:
for line in f:
pred = json.loads(line.strip())
# print(pred)
examples[pred['doc_key']]['predicted'] = pred['predicted']
examples[pred['doc_key']]['gold'] = pred['gold']
# checkpoints/gen-RAMS-pred/out_put.jsonl
writer = open(args.output_file, 'w')
for ex in tqdm(examples.values()):
if 'predicted' not in ex:# this is used for testing
continue
# get template
evt_type = get_event_type(ex)[0]
context_words = [w for sent in ex['sentences'] for w in sent ]
template = ontology_dict[evt_type.replace('n/a','unspecified')]['template']
# extract argument text
predicted_args = extract_args_from_template(ex,template, ontology_dict)
# get trigger
# extract argument span
trigger_start = ex['evt_triggers'][0][0]
trigger_end = ex['evt_triggers'][0][1]
doc = None
if args.head_only:
doc = nlp(' '.join(context_words))
for argname in predicted_args:
arg_span = find_arg_span(predicted_args[argname], context_words,
trigger_start, trigger_end, head_only=args.head_only, doc=doc)
if arg_span:# if None means hullucination
if args.head_only and args.coref:
# consider coreferential mentions as matching
assert('corefs' in ex)
gold_spans = [a[1] for a in ex['ref_evt_links'] if a[2]==argname]
arg_span = check_coref(ex, list(arg_span), gold_spans)
ex['gold_evt_links'].append([[trigger_start, trigger_end], list(arg_span), argname])
writer.write(json.dumps(ex)+'\n')
writer.close()
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