N-grams, Bigrams and Trigrams

N-gram models are essential tools in natural language processing (NLP) and computational linguistics. They help capture the frequency and co-occurrence of word sequences within a text, offering valuable insights into language patterns and structures. In particular, bigrams, trigrams, and n-grams play a pivotal role in understanding contextual relationships between adjacent words.

Understanding N-grams, Bigrams, and Trigrams

• Bigrams: Sequences of two adjacent elements (typically words) in a text.
• Trigrams: Sequences of three adjacent elements in a text.
• N-grams: Generalised sequences of 'n' adjacent elements in a text. For instance, an n-gram with n=1 is a unigram, n=2 is a bigram, n=3 is a trigram, and so forth.

Example:

• Unigram: Marketing
• Bigram: Marketing Mix
• Trigram: Marketing Mix Modelling

In this example, the bigram and trigram represent distinct concepts that carry significant meaning when used together.

Example of Bigrams and Trigrams in a Sentence

Consider the sentence: “The quick brown fox jumps over the lazy dog.”

• Bigrams: “The quick”, “quick brown”, “brown fox”, “fox jumps”, “jumps over”, “over the”, “the lazy”, “lazy dog”.
• Trigrams: “The quick brown”, “quick brown fox”, “brown fox jumps”, “fox jumps over”, “jumps over the”, “over the lazy”, “the lazy dog”.

Choosing the Right N-gram Size

Selecting the optimal size of n-grams depends on the task at hand and the trade-off between capturing meaningful context and avoiding data sparsity issues.

• Larger N-grams (higher N values): Capture more specific contextual information but may result in sparse data and computational challenges.
• Smaller N-grams (lower N values): More frequent but may lack the contextual depth necessary for complex tasks.