删除和更新用于NER训练数据的文本文档中的字符串和实体索引 [英] Deleting and updating a string and entity index in a text document for NER training data
本文介绍了删除和更新用于NER训练数据的文本文档中的字符串和实体索引的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我正在尝试创建用于NER识别的训练数据集。为此，我有大量数据需要标记并删除不必要的句子。在删除不必要的句子时，索引药水必须更新。上一天，我看到了一些用户关于这一点的令人难以置信的代码片段，现在我找不到了。修改他们的代码段，我可以简要说明我的问题
我们取一个训练样本数据：
data = [{"content":'''Hello we are hans and john. I enjoy playing Football.
I love eating grapes. Hanaan is great.''',"annotations":[{"id":1,"start":13,"end":17,"tag":"name"},
{"id":2,"start":22,"end":26,"tag":"name"},
{"id":3,"start":68,"end":74,"tag":"fruit"},
{"id":4,"start":76,"end":82,"tag":"name"}]}]
这可以使用以下空格显示代码进行可视化
import json
import spacy
from spacy import displacy
data = [{"content":'''Hello we are hans and john. I enjoy playing Football.
I love eating grapes. Hanaan is great.''',"annotations":[{"id":1,"start":13,"end":17,"tag":"name"},
{"id":2,"start":22,"end":26,"tag":"name"},
{"id":3,"start":68,"end":74,"tag":"fruit"},
{"id":4,"start":76,"end":82,"tag":"name"}]}]
annot_tags = data[data_index]["annotations"]
entities = []
for j in annot_tags:
start = j["start"]
end = j["end"]
tag = j["tag"]
entitie = (start,end,tag)
entities.append(entitie)
data_gen = (data[data_index]["content"],{"entities":entities})
data_one = []
data_one.append(data_gen)
nlp = spacy.blank('en')
raw_text = data_one[0][0]
doc = nlp.make_doc(raw_text)
spans = data_one[0][1]["entities"]
ents = []
for span_start, span_end, label in spans:
ent = doc.char_span(span_start, span_end, label=label)
if ent is None:
continue
ents.append(ent)
doc.ents = ents
displacy.render(doc, style="ent", jupyter=True)
输出将为
现在，我想删除未标记的句子并更新索引值。因此，所需的输出如下 此外，数据必须采用以下格式。删除未标记的句子，并且必须更新索引值，这样我才能获得如上所示的输出。必填输出数据
[{"content":'''Hello we are hans and john.
I love eating grapes. Hanaan is great.''',"annotations":[{"id":1,"start":13,"end":17,"tag":"name"},
{"id":2,"start":22,"end":26,"tag":"name"},
{"id":3,"start":42,"end":48,"tag":"fruit"},
{"id":4,"start":50,"end":56,"tag":"name"}]}]
我上一天关注了一篇帖子，得到了一个几乎可以工作的代码。
代码
import re
data = [{"content":'''Hello we are hans and john. I enjoy playing Football.
I love eating grapes. Hanaan is great.''',"annotations":[{"id":1,"start":13,"end":17,"tag":"name"},
{"id":2,"start":22,"end":26,"tag":"name"},
{"id":3,"start":68,"end":74,"tag":"fruit"},
{"id":4,"start":76,"end":82,"tag":"name"}]}]
for idx, each in enumerate(data[0]['annotations']):
start = each['start']
end = each['end']
word = data[0]['content'][start:end]
data[0]['annotations'][idx]['word'] = word
sentences = [ {'sentence':x.strip() + '.','checked':False} for x in data[0]['content'].split('.')]
new_data = [{'content':'', 'annotations':[]}]
for idx, each in enumerate(data[0]['annotations']):
for idx_alpha, sentence in enumerate(sentences):
if sentence['checked'] == True:
continue
temp = each.copy()
check_word = temp['word']
if check_word in sentence['sentence']:
start_idx = re.search(r'({})'.format(check_word), sentence['sentence']).start()
end_idx = start_idx + len(check_word)
current_len = len(new_data[0]['content'])
new_data[0]['content'] += sentence['sentence'] + ' '
temp.update({'start':start_idx + current_len, 'end':end_idx + current_len})
new_data[0]['annotations'].append(temp)
sentences[idx_alpha]['checked'] = True
break
print(new_data)
输出
[{'content': 'Hello we are hans and john. I love eating grapes. Hanaan is great. ',
'annotations': [{'id': 1,
'start': 13,
'end': 17,
'tag': 'name',
'word': 'hans'},
{'id': 3, 'start': 42, 'end': 48, 'tag': 'fruit', 'word': 'grapes'},
{'id': 4, 'start': 50, 'end': 56, 'tag': 'name', 'word': 'Hanaan'}]}]
约翰这个名字在这里遗失了。如果存在多个标记，我不能将其丢失
推荐答案
这是一项相当复杂的任务，因为您需要识别句子，因为对'.'进行简单的拆分可能不起作用，因为它会对'Mr.'等进行拆分。
import json
import spacy
from spacy import displacy
import re
data = [{"content":'''Hello we are hans and john. I enjoy playing Football.
I love eating grapes. Hanaan is great. Mr. Jones is nice.''',"annotations":[{"id":1,"start":13,"end":17,"tag":"name"},
{"id":2,"start":22,"end":26,"tag":"name"},
{"id":3,"start":68,"end":74,"tag":"fruit"},
{"id":4,"start":76,"end":82,"tag":"name"},
{"id":5,"start":93,"end":102,"tag":"name"}]}]
for idx, each in enumerate(data[0]['annotations']):
start = each['start']
end = each['end']
word = data[0]['content'][start:end]
data[0]['annotations'][idx]['word'] = word
text = data[0]['content']
nlp = spacy.load('en_core_web_sm')
nlp.add_pipe('sentencizer')
doc = nlp(text)
sentences = [i for i in doc.sents]
annotations = data[0]['annotations']
new_data = [{"content":'',
'annotations':[]}]
for sentence in sentences:
idx_to_remove = []
for idx, annotation in enumerate(annotations):
if annotation['word'] in sentence.text:
temp = annotation.copy()
start_idx = re.search(r'({})'.format(annotation['word']), sentence.text).start()
end_idx = start_idx + len(annotation['word'])
current_len = len(new_data[0]['content'])
temp.update({'start':start_idx + current_len, 'end':end_idx + current_len})
new_data[0]['annotations'].append(temp)
idx_to_remove.append(idx)
if len(idx_to_remove) > 0:
new_data[0]['content'] += sentence.text + ' '
for x in range(0,len(idx_to_remove)):
del annotations[0]
输出：
print(new_data)
[{'content': 'Hello we are hans and john. I love eating grapes. Hanaan is great. Mr. Jones is nice. ',
'annotations': [
{'id': 1, 'start': 13, 'end': 17, 'tag': 'name', 'word': 'hans'},
{'id': 2, 'start': 22, 'end': 26, 'tag': 'name', 'word': 'john'},
{'id': 3, 'start': 42, 'end': 48, 'tag': 'fruit', 'word': 'grapes'},
{'id': 4, 'start': 50, 'end': 56, 'tag': 'name', 'word': 'Hanaan'},
{'id': 5, 'start': 67, 'end': 76, 'tag': 'name', 'word': 'Mr. Jones'}]}]
这篇关于删除和更新用于NER训练数据的文本文档中的字符串和实体索引的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
查看全文
