Text Processing Fundamentals

Text processing forms the foundation of Natural Language Processing (NLP) systems, providing the essential techniques for converting raw text into structured formats that algorithms can effectively process. These fundamental operations transform unstructured text data into representations that capture linguistic properties and enable downstream analysis.

Tokenization and Preprocessing #

Tokenization is the process of breaking text into smaller units called tokens, which typically correspond to words, subwords, or characters. This critical first step establishes the basic units for all subsequent NLP operations.

Tokenization Approaches #

• Word Tokenization: Splits text into words based on delimiters like spaces and punctuation.
• Subword Tokenization: Breaks words into meaningful subunits, handling out-of-vocabulary words more effectively.
  • Byte-Pair Encoding (BPE): Iteratively merges the most frequent character pairs.
  • WordPiece: Similar to BPE but uses likelihood rather than frequency for merges.
  • SentencePiece: Language-independent tokenization that treats the input as a raw character sequence.
• Character Tokenization: Splits text into individual characters, useful for certain languages or specific tasks.
• Sentence Tokenization: Divides text into sentences, often using punctuation and capitalization as cues.

# Example of word tokenization

import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize
text = "Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence."
tokens = word_tokenize(text)
print(tokens)

# Using NLTK for better tokenization
from nltk.tokenize import TreebankWordTokenizer
tokenizer = TreebankWordTokenizer()
text = "Don't hesitate to email me at example@domain.com or visit our website: https://www.example.org."
tokens = tokenizer.tokenize(text)
print(tokens)

Text Preprocessing Steps #

Text preprocessing involves cleaning and standardizing text to improve the quality of subsequent analysis: