Overview
Text preprocessing is akin to preparing ingredients before cooking. It involves cleaning, normalizing, and transforming raw text, making it suitable for NLP models to process effectively.
Learning Objectives
After this lesson, you'll be able to:
- Understand the importance of text preprocessing
- Apply text cleaning and normalization
- Implement basic tokenization methods
- Differentiate between stemming and lemmatization
- Extract numerical features using BoW and TF-IDF
Why Preprocess Text?
Human language is inherently complex and varied. Preprocessing helps create consistency, allowing models to focus on meaning rather than surface variations.
Analogy: Signal Processing
Think of preprocessing as cleaning an audio signal—removing noise and normalizing volume to enhance clarity, much like tuning a radio to get a clear signal without static.
Text Cleaning and Normalization
Imagine you're editing a manuscript. You would:
- Remove unnecessary formatting (HTML tags)
- Standardize the text style (lowercasing)
- Eliminate distractions (punctuation and numbers)
- Focus on key words (removing stopwords)
- Clarify meanings (handling contractions)
Before/After Examples
| Step | Before | After |
|---|---|---|
| Strip HTML | "<p>Hello, <b>World</b>!</p>" | "Hello, World!" |
| Lowercase | "New York CITY" | "new york city" |
| Normalize punctuation | "GPU(s): 2xA100!!!" | "gpu s 2xa100" |
| Expand contractions | "don't, it's" | "do not, it is" |
| Remove stopwords | "this is the best book" | "best book" |
Try It Yourself: Basic Text Cleaning
Downstream Impact (toy example)
| Preprocessing | Sentiment accuracy |
|---|---|
| None | 74% |
| Lowercase + punctuation cleaning | 79% |
| + stopwords removal | 81% |
| + lemmatization | 83% |
Note: Gains depend on language and task; avoid over-normalizing domain terms (e.g., chemical names, code, product SKUs).
Tokenization
Tokenization is like breaking a sentence into words or meaningful pieces—essential for understanding and processing language.
Types of Tokenization
- Word Tokenization: Breaking text into individual words.
- Character Tokenization: Breaking text into characters for languages like Chinese.
- N-gram Tokenization: Creating tokens of contiguous characters or words, useful for capturing local context.
- Subword Tokenization: A balance between character and word tokenization, often used in modern NLP to handle rare words more effectively.
Try It Yourself: Tokenization Comparison
Stemming vs. Lemmatization
- Stemming: Simplifying words down to their base form quickly, though sometimes inaccurately.
- Lemmatization: More accurately reducing words to their dictionary forms based on vocabulary and morphological analysis.
Comparison Example
| Word | Stemming (Porter) | Lemmatization |
|---|---|---|
| running | run | run |
| better | better | good |
| studies | studi | study |
| was | wa | be |
Try It Yourself: Stemming Demo
Feature Extraction
Transforming text into numerical representations is crucial for machine learning models. Techniques like BoW count words, while TF-IDF adjusts these counts based on their document frequency to highlight more important words.
Common Pitfalls
- Over-aggressive normalization can remove meaning (e.g., dropping negations: "not good" → "good").
- Lemmatization/stemming may harm performance for morphologically rich languages if misconfigured.
- Removing stopwords blindly can break phrase-level meaning ("to be or not to be").
Practical Considerations
When preprocessing text, consider the language's characteristics, the specific requirements of the task, computational constraints, and the domain's specificity. Avoid over-simplifying text or missing out on crucial nuances.
Interactive Exploration: Text Preprocessing Pipeline
Explore text preprocessing interactively with our tool, which allows you to see the effects of different preprocessing techniques in real-time.