文本分类的数据增强

文本分类的数据增强可以通过以下方法来解决，其中包含了代码示例：

1. 同义词替换：使用WordNet等工具库，将文本中的某些词替换为其同义词，以扩充语料库。代码示例：

import nltk
from nltk.corpus import wordnet

def synonym_replacement(text, n):
    words = nltk.word_tokenize(text)
    new_words = words.copy()
    random_word_list = list(set([word for word in 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:  # 替换n个单词后退出循环
            break
    new_text = ' '.join(new_words)
    return new_text

def get_synonyms(word):
    synonyms = []
    for syn in wordnet.synsets(word):
        for lemma in syn.lemmas():
            synonyms.append(lemma.name())
    return synonyms

2. 随机插入：在文本中随机位置插入一些额外的词语，以增加多样性。代码示例：

import random

def random_insertion(text, n):
    words = nltk.word_tokenize(text)
    new_words = words.copy()
    for _ in range(n):
        word = random.choice(words)
        new_words.insert(random.randrange(len(new_words)+1), word)
    new_text = ' '.join(new_words)
    return new_text

3. 随机删除：随机删除文本中的某些词语，以模拟噪音或缺失数据的情况。代码示例：

def random_deletion(text, p):
    words = nltk.word_tokenize(text)
    if len(words) == 1:
        return text
    remaining_words = []
    for word in words:
        if random.uniform(0, 1) > p:
            remaining_words.append(word)
    if len(remaining_words) == 0:
        return ' '.join(words)
    new_text = ' '.join(remaining_words)
    return new_text

4. 随机交换：随机交换文本中的两个词语的位置，以增加多样性。代码示例：

def random_swap(text, n):
    words = nltk.word_tokenize(text)
    new_words = words.copy()
    for _ in range(n):
        new_words = swap_words(new_words)
    new_text = ' '.join(new_words)
    return new_text

def swap_words(words):
    if len(words) > 1:
        idx1 = random.randint(0, len(words)-1)
        idx2 = random.randint(0, len(words)-1)
        words[idx1], words[idx2] = words[idx2], words[idx1]
    return words

这些方法可以根据具体的需求和数据集进行组合和调整，以实现更好的数据增强效果。