How Can We Speed Up Regex Replacements for Removing Words from Millions of Sentences in Python?

Speeding Up Regex Replacements in Python

Problem

The following Python code aims to efficiently remove specific words from a large collection of sentences, ensuring that replacements only occur at word boundaries:

import re

for sentence in sentences:
  for word in compiled_words:
    sentence = re.sub(word, "", sentence)

While this approach works, it's slow, taking hours to process millions of sentences. Exploring faster solutions is necessary.

Faster Regex Method

An optimized version of the regex approach can significantly improve performance. Instead of using a slow regex union, which becomes inefficient as the number of banned words increases, a Trie-based regex can be crafted.

A Trie is a data structure that organizes banned words efficiently. By utilizing a Trie, a single regex pattern can be generated that accurately replaces words at word boundaries without the performance overhead of checking each word individually.

This Trie-based regex approach can be implemented using the following steps:

1. Construct a Trie data structure from the banned words.
2. Convert the Trie into a regex pattern.
3. Utilize the regex pattern for efficient word replacements.

Set-Based Approach

For situations where regex isn't suitable, a faster alternative is possible using a set-based approach.

1. Construct a set of banned words.
2. For each sentence, split it into words.
3. Remove banned words from the list of split words.
4. Reconstruct the sentence from the modified word list.

This method avoids the overhead of regular expression matching, but its speed depends on the size of the banned word set.

Additional Optimizations

To further enhance performance, consider additional optimizations:

• Pre-compile your banned word patterns for both regex and set-based methods.
• Parallelize the replacement process across multiple CPU cores.
• Consider using a pre-trained language model for word identification and removal.

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