Dive into DL: Text Prepare

文本预处理

文本是一类序列数据,一篇文章可以看作是字符或单词的序列,本节将介绍文本数据的常见预处理步骤,预处理通常包括四个步骤:

  1. 读入文本
  2. 分词
  3. 建立字典,将每个词映射到一个唯一的索引(index)
  4. 将文本从词的序列转换为索引的序列,方便输入模型

读入文本

我们用一部英文小说,即H. G. Well的Time Machine,作为示例,展示文本预处理的具体过程。

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import collections
import re

def read_time_machine():
with open('./input/timemachine.txt', 'r', encoding='utf-8') as f:
lines = [re.sub('[^a-z]+', ' ', line.strip().lower()) for line in f]
return lines


lines = read_time_machine()
print('# sentences %d' % len(lines))
# sentences 3583

分词

我们对每个句子进行分词,也就是将一个句子划分成若干个词(token),转换为一个词的序列。

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def tokenize(sentences, token='word'):
"""Split sentences into word or char tokens"""
if token == 'word':
return [sentence.split(' ') for sentence in sentences]
elif token == 'char':
return [list(sentence) for sentence in sentences]
else:
print('ERROR: unkown token type '+token)

tokens = tokenize(lines)
tokens[0:2]
[['the', 'time', 'machine', 'by', 'h', 'g', 'wells', ''], ['']]

建立字典

为了方便模型处理,我们需要将字符串转换为数字。因此我们需要先构建一个字典(vocabulary),将每个词映射到一个唯一的索引编号。

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class Vocab(object):
def __init__(self, tokens, min_freq=0, use_special_tokens=False):
counter = count_corpus(tokens) # :
self.token_freqs = list(counter.items())
self.idx_to_token = []
if use_special_tokens:
# padding, begin of sentence, end of sentence, unknown
self.pad, self.bos, self.eos, self.unk = (0, 1, 2, 3)
self.idx_to_token += ['', '', '', '']
else:
self.unk = 0
self.idx_to_token += ['']
self.idx_to_token += [token for token, freq in self.token_freqs
if freq >= min_freq and token not in self.idx_to_token]
self.token_to_idx = dict()
for idx, token in enumerate(self.idx_to_token):
self.token_to_idx[token] = idx

def __len__(self):
return len(self.idx_to_token)

def __getitem__(self, tokens):
if not isinstance(tokens, (list, tuple)):
return self.token_to_idx.get(tokens, self.unk)
return [self.__getitem__(token) for token in tokens]

def to_tokens(self, indices):
if not isinstance(indices, (list, tuple)):
return self.idx_to_token[indices]
return [self.idx_to_token[index] for index in indices]

def count_corpus(sentences):
tokens = [tk for st in sentences for tk in st]
return collections.Counter(tokens) # 返回一个字典,记录每个词的出现次数

我们看一个例子,这里我们尝试用Time Machine作为语料构建字典

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vocab = Vocab(tokens)
print(list(vocab.token_to_idx.items())[0:10])
[('', 0), ('the', 1), ('time', 2), ('machine', 3), ('by', 4), ('h', 5), ('g', 6), ('wells', 7), ('i', 8), ('traveller', 9)]

将词转为索引

使用字典,我们可以将原文本中的句子从单词序列转换为索引序列

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for i in range(8, 10):
print('words:', tokens[i])
print('indices:', vocab[tokens[i]])
words: ['the', 'time', 'traveller', 'for', 'so', 'it', 'will', 'be', 'convenient', 'to', 'speak', 'of', 'him', '']
indices: [1, 2, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 0]
words: ['was', 'expounding', 'a', 'recondite', 'matter', 'to', 'us', 'his', 'grey', 'eyes', 'shone', 'and']
indices: [20, 21, 22, 23, 24, 16, 25, 26, 27, 28, 29, 30]

用现有工具进行分词

我们前面介绍的分词方式非常简单,它至少有以下几个缺点:

  1. 标点符号通常可以提供语义信息,但是我们的方法直接将其丢弃了
  2. 类似“shouldn't", "doesn't"这样的词会被错误地处理
  3. 类似"Mr.", "Dr."这样的词会被错误地处理

我们可以通过引入更复杂的规则来解决这些问题,但是事实上,有一些现有的工具可以很好地进行分词,我们在这里简单介绍其中的两个:spaCyNLTK

下面是一个简单的例子:

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text = "Mr. Chen doesn't agree with my suggestion."

spaCy:

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import spacy
nlp = spacy.load('en_core_web_sm')
doc = nlp(text)
print([token.text for token in doc])
['Mr.', 'Chen', 'does', "n't", 'agree', 'with', 'my', 'suggestion', '.']

NLTK:

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from nltk.tokenize import word_tokenize
from nltk import data
data.path.append('/home/kesci/input/nltk_data3784/nltk_data')
print(word_tokenize(text))
['Mr.', 'Chen', 'does', "n't", 'agree', 'with', 'my', 'suggestion', '.']

Dive into DL: Text Prepare
http://chenxindaaa.com/Artificial-Intelligence/DL/NLP/Dive into DL/Dive into DL-Text Prepare/
Author
chenxindaaa
Posted on
February 14, 2020
Licensed under