Bigram

Bigram A bigram is a sequence of two adjacent elements from a string of tokens, which are typically letters, syllables, or words. The concept of bigrams is widely used in various fields such as linguistics, cryptography, and natural language processing (NLP).

Applications

Bigrams are used in several applications, including:

• Text mining: Bigrams help in understanding the context and meaning of text by analyzing the frequency and patterns of word pairs.
• Speech recognition: Bigrams assist in predicting the next word in a sequence, improving the accuracy of speech recognition systems.
• Machine translation: Bigrams are used to enhance the quality of translations by considering the relationship between word pairs.
• Information retrieval: Bigrams improve search engine results by considering the proximity of words in a query.

Calculation

The frequency of bigrams in a text can be calculated using the following formula: <math> P(w_i | w_{i-1}) = \frac{C(w_{i-1}, w_i)}{C(w_{i-1})} </math> where:

• <math> P(w_i | w_{i-1}) </math> is the probability of word <math> w_i </math> given the previous word <math> w_{i-1} </math>.
• <math> C(w_{i-1}, w_i) </math> is the count of the bigram <math> (w_{i-1}, w_i) </math>.
• <math> C(w_{i-1}) </math> is the count of the word <math> w_{i-1} </math>.

Examples

In the sentence "The quick brown fox jumps over the lazy dog," the bigrams are:

• The quick
• quick brown
• brown fox
• fox jumps
• jumps over
• over the
• the lazy
• lazy dog

Related Concepts

• Unigram
• Trigram
• N-gram
• Markov model
• Language model

See Also

• Natural language processing
• Text mining
• Speech recognition
• Machine translation
• Information retrieval