Text Preprocessing

:label:sec_text_preprocessing

We have reviewed and evaluated statistical tools and prediction challenges for sequence data. Such data can take many forms. Specifically, as we will focus on in many chapters of the book, text is one of the most popular examples of sequence data. For example, an article can be simply viewed as a sequence of words, or even a sequence of characters. To facilitate our future experiments with sequence data, we will dedicate this section to explain common preprocessing steps for text. Usually, these steps are:

  1. Load text as strings into memory.
  2. Split strings into tokens (e.g., words and characters).
  3. Build a table of vocabulary to map the split tokens to numerical indices.
  4. Convert text into sequences of numerical indices so they can be manipulated by models easily.

```{.python .input} import collections from d2l import mxnet as d2l import re

  2. ```{.python .input}
  3. #@tab pytorch
  4. import collections
  5. from d2l import torch as d2l
  6. import re

```{.python .input}

@tab tensorflow

import collections from d2l import tensorflow as d2l import re

  2. ## Reading the Dataset
  4. To get started we load text from H. G. Wells' [*The Time Machine*](http://www.gutenberg.org/ebooks/35).
  5. This is a fairly small corpus of just over 30000 words, but for the purpose of what we want to illustrate this is just fine.
  6. More realistic document collections contain many billions of words.
  7. The following function reads the dataset into a list of text lines, where each line is a string.
  8. For simplicity, here we ignore punctuation and capitalization.
  10. ```{.python .input}
  11. #@tab all
  12. #@save
  13. d2l.DATA_HUB['time_machine'] = (d2l.DATA_URL + 'timemachine.txt',
  14.                                 '090b5e7e70c295757f55df93cb0a180b9691891a')
  16. def read_time_machine():  #@save
  17.     """Load the time machine dataset into a list of text lines."""
  18.     with open(d2l.download('time_machine'), 'r') as f:
  19.         lines = f.readlines()
  20.     return [re.sub('[^A-Za-z]+', ' ', line).strip().lower() for line in lines]
  22. lines = read_time_machine()
  23. print(f'# text lines: {len(lines)}')
  24. print(lines[0])
  25. print(lines[10])

Tokenization

The following tokenize function takes a list (lines) as the input, where each list is a text sequence (e.g., a text line). Each text sequence is split into a list of tokens. A token is the basic unit in text. In the end, a list of token lists are returned, where each token is a string.

```{.python .input}

@tab all

def tokenize(lines, token=’word’): #@save “””Split text lines into word or character tokens.””” if token == ‘word’: return [line.split() for line in lines] elif token == ‘char’: return [list(line) for line in lines] else: print(‘ERROR: unknown token type: ‘ + token)

tokens = tokenize(lines) for i in range(11): print(tokens[i])

  2. ## Vocabulary
  4. The string type of the token is inconvenient to be used by models, which take numerical inputs.
  5. Now let us build a dictionary, often called *vocabulary* as well, to map string tokens into numerical indices starting from 0.
  6. To do so, we first count the unique tokens in all the documents from the training set,
  7. namely a *corpus*,
  8. and then assign a numerical index to each unique token according to its frequency.
  9. Rarely appeared tokens are often removed to reduce the complexity.
  10. Any token that does not exist in the corpus or has been removed is mapped into a special unknown token “<unk>”.
  11. We optionally add a list of reserved tokens, such as
  12. “<pad>” for padding,
  13. “<bos>” to present the beginning for a sequence, and “<eos>” for the end of a sequence.
  15. ```{.python .input}
  16. #@tab all
  17. class Vocab:  #@save
  18.     """Vocabulary for text."""
  19.     def __init__(self, tokens=None, min_freq=0, reserved_tokens=None):
  20.         if tokens is None:
  21.             tokens = []
  22.         if reserved_tokens is None:
  23.             reserved_tokens = [] 
  24.         # Sort according to frequencies
  25.         counter = count_corpus(tokens)
  26.         self.token_freqs = sorted(counter.items(), key=lambda x: x[0])
  27.         self.token_freqs.sort(key=lambda x: x[1], reverse=True)
  28.         # The index for the unknown token is 0
  29.         self.unk, uniq_tokens = 0, ['<unk>'] + reserved_tokens
  30.         uniq_tokens += [token for token, freq in self.token_freqs
  31.                         if freq >= min_freq and token not in uniq_tokens]
  32.         for token in uniq_tokens:
  33.             self.idx_to_token.append(token)
  34.             self.token_to_idx[token] = len(self.idx_to_token) - 1
  36.     def __len__(self):
  37.         return len(self.idx_to_token)
  39.     def __getitem__(self, tokens):
  40.         if not isinstance(tokens, (list, tuple)):
  41.             return self.token_to_idx.get(tokens, self.unk)
  42.         return [self.__getitem__(token) for token in tokens]
  44.     def to_tokens(self, indices):
  45.         if not isinstance(indices, (list, tuple)):
  46.             return self.idx_to_token[indices]
  47.         return [self.idx_to_token[index] for index in indices]
  49. def count_corpus(tokens):  #@save
  50.     """Count token frequencies."""
  51.     # Here `tokens` is a 1D list or 2D list
  52.     if len(tokens) == 0 or isinstance(tokens[0], list):
  53.         # Flatten a list of token lists into a list of tokens
  54.         tokens = [token for line in tokens for token in line]
  55.     return collections.Counter(tokens)

We construct a vocabulary using the time machine dataset as the corpus. Then we print the first few frequent tokens with their indices.

```{.python .input}

@tab all

vocab = Vocab(tokens) print(list(vocab.token_to_idx.items())[:10])

  2. Now we can convert each text line into a list of numerical indices.
  4. ```{.python .input}
  5. #@tab all
  6. for i in [0, 10]:
  7.     print('words:', tokens[i])
  8.     print('indices:', vocab[tokens[i]])

Putting All Things Together

Using the above functions, we package everything into the load_corpus_time_machine function, which returns corpus, a list of token indices, and vocab, the vocabulary of the time machine corpus. The modifications we did here are: i) we tokenize text into characters, not words, to simplify the training in later sections; ii) corpus is a single list, not a list of token lists, since each text line in the time machine dataset is not necessarily a sentence or a paragraph.

```{.python .input}

@tab all

def load_corpus_time_machine(max_tokens=-1): #@save “””Return token indices and the vocabulary of the time machine dataset.””” lines = read_time_machine() tokens = tokenize(lines, ‘char’) vocab = Vocab(tokens)

  1. # Since each text line in the time machine dataset is not necessarily a
  2. # sentence or a paragraph, flatten all the text lines into a single list
  3. corpus = [vocab[token] for line in tokens for token in line]
  4. if max_tokens > 0:
  5.     corpus = corpus[:max_tokens]
  6. return corpus, vocab

corpus, vocab = load_corpus_time_machine() len(corpus), len(vocab) ```

Summary

  • Text is an important form of sequence data.
  • To preprocess text, we usually split text into tokens, build a vocabulary to map token strings into numerical indices, and convert text data into token indices for models to manipulate.

Exercises

  1. Tokenization is a key preprocessing step. It varies for different languages. Try to find another three commonly used methods to tokenize text.
  2. In the experiment of this section, tokenize text into words and vary the min_freq arguments of the Vocab instance. How does this affect the vocabulary size?

Discussions