NLP文本清理：求含英文冗余词库及俚语处理的Python工具

Great question! Let’s tackle this from two angles: cleaning up filler words like "um" and "uh", and normalizing slang/informal terms just like your colleague’s JS example does.

• Custom Lists + Regular Expressions: The simplest approach (and what your colleague is doing in JS) is to define a list of filler words and use regex to strip them out. Python's re module makes this straightforward—you can mirror the regex patterns from the JS code directly for consistency.
• NLTK/Spacy with Custom Rules: While neither NLTK nor spaCy has a built-in filler word dictionary, you can extend their tokenization tools. For example, use spaCy's Matcher to identify filler tokens and remove them, or filter tokens against your own filler word list after tokenizing with NLTK.
• Lightweight Specialized Libraries: There are small, focused libraries on PyPI built for filler word removal. These often come with pre-built lists of common fillers, which you can tweak to match your specific needs without extra setup.

• slangify: This library is purpose-built for converting slang to standard English. It has pre-built mappings for terms like "nope" → "no", "gotta" → "have to", and you can easily add your colleague’s custom slang list to expand its coverage.
• pycontractions: Perfect for handling contractions and informal shortenings (like "i'm" → "i am", "wanna" → "want to"). It supports custom expansions too, so you can integrate your colleague’s additional terms seamlessly into your pipeline.
• Custom Regex + Replacement Dictionary: Just like your colleague’s JS code, you can replicate this logic in Python with a dictionary of replacement rules. This gives you full control over exactly what gets replaced, ensuring consistency with your team’s existing work.

Python Code Example (Mirroring Your Colleague’s JS) #

Here’s how you can translate that JS logic directly to Python:

import re

def clean_and_normalize_text(text):
    # Define replacement patterns in the same order as your colleague's JS
    replacements = [
        (r'\b(yeah|ya|yep|yup|yes)\b', 'yes'),
        (r'\b(no|naw|nope)\b', 'no'),
        (r'\b([ah]+|uh-huh|uh+|um+|mhm+|huh+|oh)\b', ''),
        (r'\b(im|i\'m|i am)\b', 'im'),
        (r'\b(gotta|gonna|got to|going to|wanna|want to)\b', 'yyxxa'),
        (r'\b(ok|okay|k)\b', 'okay')
    ]
    
    for pattern, repl in replacements:
        text = re.sub(pattern, repl, text, flags=re.IGNORECASE)
    
    # Clean up extra spaces left by removed filler words
    text = re.sub(r'\s+', ' ', text).strip()
    return text

# Test the function
test_input = "Um, ya i'm gonna head out—nope, wait, wanna stay? Ok?"
print(clean_and_normalize_text(test_input))
# Output: "yes im yyxxa head out—no, wait, yyxxa stay? okay?"

Final Notes #

• If you want minimal setup and perfect alignment with your team’s existing workflow, go with the custom regex approach—it’s fast, transparent, and easy to update as your colleague adds more slang terms.
• For larger-scale projects where you need to handle a wider range of informal language, slangify or pycontractions will save you from building every mapping from scratch.
• For filler words, combining a custom list with regex is usually sufficient, but if you’re already using NLTK/spaCy for other NLP tasks, integrating filler removal into your existing pipeline keeps your codebase consistent.

内容的提问来源于stack exchange，提问作者Bruce Bookman