Text preprocessing is a crucial step in natural language processing (NLP) that involves cleaning and transforming raw text data into a format suitable for further analysis and modeling. Tokenisation is one of the fundamental tasks in text preprocessing, and it involves breaking down text into smaller units, typically words or tokens. Here’s a more detailed explanation of both:

Text Preprocessing:

1. Cleaning:
   • Removing special characters, punctuation, and non-alphanumeric characters.
   • Handling capitalization (converting text to lowercase or uppercase).
   • Removing HTML tags, URLs, or any other irrelevant information.
   • Handling contractions (e.g., converting “don’t” to “do not”).
2. Normalization:
   • Standardizing text by converting abbreviations or slang to their full forms.
   • Handling accented characters or diacritics by converting them to their ASCII equivalents.
   • Correcting spelling mistakes using spell-checking algorithms or dictionaries.
   • Removing stopwords (commonly occurring words like “the,” “and,” “of”) that do not contribute much to the meaning of the text.
3. Tokenization:
   • Breaking down text into smaller units called tokens, which can be words, subwords, or characters.
   • Tokens are the basic building blocks used for further analysis and modeling in NLP tasks.
4. Stemming and Lemmatization:
   • Reducing words to their root form to normalize variations of the same word.
   • Stemming chops off prefixes or suffixes to obtain the root form (e.g., “running” → “run”).
   • Lemmatization uses vocabulary and morphological analysis to return the base or dictionary form of a word (e.g., “better” → “good”).

Tokenisation:

1. Word Tokenisation:
   • Splitting text into words based on whitespace or punctuation boundaries.
   • Handling contractions, possessives, hyphenated words, and other special cases.
2. Sentence Tokenisation:
   • Splitting text into sentences based on punctuation marks like periods, exclamation marks, and question marks.
   • Handling abbreviations, initials, and other cases where periods occur within a sentence.
3. Subword Tokenisation:
   • Breaking down words into smaller subword units, useful for handling out-of-vocabulary words and morphologically rich languages.
   • Techniques like Byte Pair Encoding (BPE), WordPiece, and SentencePiece are commonly used for subword tokenisation.
4. Character Tokenisation:
   • Breaking down text into individual characters, useful for tasks like text generation and character-level language modeling.
   • Preserves the sequential nature of text data but may require larger vocabularies and more computational resources.

Text preprocessing and tokenisation are essential for cleaning noisy text data, reducing vocabulary size, and transforming text into a format suitable for NLP tasks like text classification, sentiment analysis, machine translation, and more. These steps help improve the quality and efficiency of downstream NLP models and algorithms.