Text Processing Basics

Introduction

Text processing is a foundational concept in full-text search databases, enabling efficient searching and indexing of textual data. This lesson covers essential aspects of text processing, including preprocessing, tokenization, normalization, and stemming.

Key Concepts

• Tokenization: The process of breaking text into smaller components, called tokens.
• Normalization: Adjusting tokens to a standard format (e.g., lowercasing).
• Stemming and Lemmatization: Reducing words to their base form.
• Stop Words: Commonly used words that may be filtered out during processing.
• N-grams: Sequences of 'n' items from a given sample of text.

Text Processing Steps

The text processing workflow typically consists of the following steps:

1. Collect Raw Text Data
2. Tokenization
3. Normalization
   
   Tip: Always convert text to lowercase for consistency.
4. Removing Stop Words
5. Stemming or Lemmatization
6. Generating N-grams

Example Code for Tokenization

import re

def tokenize(text):
    # Remove punctuation and split by whitespace
    return re.findall(r'\b\w+\b', text.lower())

text = "Hello, world! Welcome to text processing."
tokens = tokenize(text)
print(tokens)  # Output: ['hello', 'world', 'welcome', 'to', 'text', 'processing']
                

Best Practices

• Always preprocess data before indexing.
• Choose appropriate stemming or lemmatization techniques based on use case.
• Regularly update stop words list to match the context of your data.
• Utilize n-grams for applications requiring phrase matching.
• Benchmark different tokenization methods to find the most efficient one.

FAQ