Text Preprocessing Techniques for Tokenization
Understanding Tokenization
Tokenization is the fundamental process of breaking down a text into individual units called
tokens. These tokens can be words, sentences, or even subword units, depending on the
specific application. This step is crucial in natural language processing (NLP) as it lays the
foundation for further analysis and tasks like sentiment analysis, machine translation, and
information retrieval.
Common Text Preprocessing Techniques
1. Case Normalization:
oLowercasing: Converts all characters to lowercase, ensuring consistency.
oUppercasing: Converts all characters to uppercase, useful for specific
applications like named entity recognition.
2. Punctuation Handling:
oRemoval: Removes punctuation marks that might interfere with analysis.
oPreservation: Retains punctuation if it's essential for the task (e.g., sentiment
analysis).
3. Stop Word Removal:
oIdentification: Removes commonly occurring words (stop words) that add
little semantic value.
oCustomizable: Allows for the creation of custom stop word lists based on
specific requirements.
4. Stemming and Lemmatization:
oStemming: Reduces words to their root or stem form.
oLemmatization: Identifies the base form of a word, considering part of
speech and morphological rules.
5. Number Handling:
oNormalization: Converts numbers to a standard format or replaces them with
tokens.
oPreservation: Retains numbers if they're important for the task.
6. Special Character Handling:
