Text Preprocessing & Tokenization

Introduction

Text preprocessing and tokenization are the foundational steps in any Natural Language Processing (NLP) pipeline. Before machines can understand and analyze text, we need to break it down into manageable pieces and clean it up. This process transforms raw text into a structured format that algorithms can work with.

In this module, you'll learn how to tokenize text, normalize it, and prepare it for downstream NLP tasks like sentiment analysis, text classification, and language modeling.

What is Tokenization?

Tokenization is the process of breaking text into smaller units called tokens. These tokens can be:

Words: "The quick brown fox" → "The", "quick", "brown", "fox"
Sentences: "Hello world. How are you?" → "Hello world.", "How are you?"
Characters: "Hello" → "H", "e", "l", "l", "o"
Subwords: "unhappiness" → "un", "happiness"

The choice of tokenization strategy depends on your specific NLP task and language characteristics.

Why is Tokenization Important?

Tokenization serves several critical purposes:

Vocabulary Building: Creates a dictionary of unique words/tokens in your corpus
Feature Extraction: Converts text into numerical representations for machine learning
Text Analysis: Enables counting, frequency analysis, and pattern detection
Normalization: Standardizes text for consistent processing
Dimensionality Reduction: Reduces the complexity of text data

Tokenization Strategies

Word Tokenization

The most common approach, splitting text by whitespace and punctuation:

Input: "Machine learning is amazing!"
Output: ["Machine", "learning", "is", "amazing", "!"]

Advantages:

Intuitive and easy to understand
Works well for many languages
Preserves semantic meaning

Challenges:

Handling contractions ("don't" → "do" + "n't"?)
Compound words ("New York", "ice cream")
Punctuation attachment

Sentence Tokenization

Splits text into sentences based on punctuation marks:

Input: "Hello world. How are you? I'm fine!"
Output: ["Hello world.", "How are you?", "I'm fine!"]

Use Cases:

Document summarization
Sentiment analysis per sentence
Machine translation

Challenges:

Abbreviations (Dr., Mr., etc.)
Decimal numbers (3.14)
Ellipsis (...)

Character Tokenization

Breaks text into individual characters:

Input: "Hello"
Output: ["H", "e", "l", "l", "o"]

Advantages:

No out-of-vocabulary issues
Works for any language
Useful for character-level models

Disadvantages:

Loses word-level semantics
Creates very long sequences
Requires more computation

Text Normalization

Normalization standardizes text to reduce variability:

Lowercasing

Converts all text to lowercase:

"Machine Learning" → "machine learning"

Benefits:

Reduces vocabulary size
Treats "The" and "the" as the same word
Improves model generalization

Drawbacks:

Loses information (proper nouns, acronyms)
"US" (country) vs "us" (pronoun)

Punctuation Removal

Strips punctuation marks from text:

"Hello, world!" → "Hello world"

When to Use:

Topic modeling
Text classification
When punctuation doesn't carry meaning

When to Keep:

Sentiment analysis (! and ? matter)
Named entity recognition
Question answering

Stopword Removal

Filters out common words that carry little meaning:

"the quick brown fox" → "quick brown fox"

Common Stopwords: the, is, at, which, on, a, an, and, or, but

Benefits:

Reduces noise in text data
Decreases vocabulary size
Focuses on content words

Considerations:

May remove important context
"not" is often a stopword but crucial for sentiment
Domain-specific stopwords may differ

Stemming

Reduces words to their root form by removing suffixes:

"running" → "run"
"happily" → "happi"
"studies" → "studi"

Algorithms:

Porter Stemmer: Most common, rule-based
Lancaster Stemmer: More aggressive
Snowball Stemmer: Multilingual support

Advantages:

Reduces vocabulary size
Groups related words together
Fast and simple

Disadvantages:

May produce non-words ("happi")
Can be too aggressive
Language-specific rules needed

Building a Vocabulary

After tokenization, we create a vocabulary - the set of unique tokens:

Text: "the cat sat on the mat"
Tokens: ["the", "cat", "sat", "on", "the", "mat"]
Vocabulary: {"the", "cat", "sat", "on", "mat"}
Vocabulary Size: 5

Token Frequency

Counting how often each token appears:

"the": 2
"cat": 1
"sat": 1
"on": 1
"mat": 1

Applications:

TF-IDF weighting
Feature selection
Rare word filtering

Interactive Demo

Use the controls to experiment with different tokenization and normalization options:

Choose a Sample Text: Start with different text examples
Select Tokenization Mode: Try word, sentence, or character tokenization
Apply Normalization: Toggle lowercase conversion
Filter Tokens: Remove punctuation and stopwords
Apply Stemming: See how words are reduced to roots

Observe how each option affects:

The number of tokens
Vocabulary size
Token types and frequencies

Use Cases

Text Classification

Tokenization prepares text for classification tasks:

Spam detection
Sentiment analysis
Topic categorization

Information Retrieval

Search engines use tokenization to:

Index documents
Match queries to content
Rank results by relevance

Machine Translation

Translation systems tokenize to:

Align source and target languages
Handle word order differences
Manage vocabulary

Text Generation

Language models tokenize to:

Learn word patterns
Generate coherent text
Predict next words

Best Practices

1. Understand Your Task

Different tasks require different preprocessing:

Sentiment Analysis: Keep punctuation and capitalization
Topic Modeling: Remove stopwords and apply stemming
Named Entity Recognition: Preserve capitalization

2. Preserve Important Information

Don't over-normalize:

Keep negations for sentiment ("not good" ≠ "good")
Preserve proper nouns for entity recognition
Maintain punctuation for question detection

3. Handle Special Cases

Consider:

URLs and email addresses
Hashtags and mentions (@user)
Numbers and dates
Emojis and special characters

4. Language-Specific Processing

Different languages need different approaches:

English: Space-separated words
Chinese/Japanese: No spaces between words
Arabic: Right-to-left text
German: Compound words

5. Consistency is Key

Apply the same preprocessing to:

Training data
Validation data
Test data
Production inputs

Common Pitfalls

Over-Preprocessing

Removing too much information:

Losing semantic meaning
Reducing model performance
Creating ambiguity

Under-Preprocessing

Not cleaning enough:

Large vocabulary size
Sparse features
Poor generalization

Inconsistent Preprocessing

Different preprocessing for train vs. test:

Distribution mismatch
Poor model performance
Unexpected errors

Advanced Topics

Subword Tokenization

Modern approaches like Byte-Pair Encoding (BPE) and WordPiece:

Balance between word and character tokenization
Handle rare and unknown words
Used in BERT, GPT, and other transformers

Contextual Tokenization

Consider surrounding context:

"bank" (financial) vs "bank" (river)
Homonyms and polysemy
Part-of-speech tagging

Multilingual Tokenization

Handling multiple languages:

Unicode normalization
Script detection
Language-specific rules

Summary

Text preprocessing and tokenization are essential first steps in NLP:

Tokenization breaks text into manageable units (words, sentences, characters)
Normalization standardizes text (lowercasing, stemming, stopword removal)
Vocabulary building creates a dictionary of unique tokens
Token frequency analysis reveals patterns in text data

The right preprocessing strategy depends on your specific task, language, and data characteristics. Experiment with different options to find what works best for your use case!

Text Preprocessing & Tokenization

Interactive Exploration

Controls

Input

Tokenization

Normalization

Filtering

Visualization

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