数据增强代码
时间: 2023-07-27 20:06:01 浏览: 40
以下是一个简单的数据增强函数示例,使用了Python中的NLTK库:
```python
import nltk
from nltk.corpus import wordnet
# 定义一个函数,将单词替换成它的同义词
def synonym_replacement(words, n):
new_words = words.copy()
random_word_list = list(set([word for word in words if word not in stop_words]))
random.shuffle(random_word_list)
num_replaced = 0
for random_word in random_word_list:
synonyms = get_synonyms(random_word)
if len(synonyms) >= 1:
synonym = random.choice(synonyms)
new_words = [synonym if word == random_word else word for word in new_words]
num_replaced += 1
if num_replaced >= n: # 只替换n个单词
break
# 将新的句子拼接起来
sentence = ' '.join(new_words)
return sentence
# 获取单词的同义词列表
def get_synonyms(word):
synonyms = []
for syn in wordnet.synsets(word):
for lemma in syn.lemmas():
synonym = lemma.name().replace('_', ' ').replace('-', ' ').lower()
if synonym != word and synonym not in synonyms:
synonyms.append(synonym)
return synonyms
```
此函数将原始文本中的单词替换为其同义词,并返回新的文本。您可以使用此函数来增加您的文本数据集,以便训练机器学习模型。