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

本教程将介绍删除和更新用于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，这篇教程是从别的地方看到的，然后加了一些国外程序员的疑问与解答，希望能对你有所帮助，好了，下面开始学习吧。

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

Output 1

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

Required Output

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。

既然您使用Spacy，为什么不让它识别句子，然后遍历这些句子并计算出那些开始和结束索引，而不包括任何没有实体句子。然后重新构建内容。

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'}]}]

好了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教程就到这里就结束了，希望趣模板源码网找到的这篇技术文章能帮助到大家，更多技术教程可以在站内搜索。