A bigram or digram is a sequence of two adjacent elements from a string of tokens, which are typically letters, syllables, or words. A bigram is an n-gram for n=2.
The frequency distribution of every bigram in a string is commonly used for simple statistical analysis of text in many applications, including in computational linguistics, cryptography, and speech recognition.
Gappy bigrams or skipping bigrams are word pairs which allow gaps (perhaps avoiding connecting words, or allowing some simulation of dependencies, as in a dependency grammar).
Bigrams, along with other n-grams, are used in most successful language models for speech recognition.
Bigram frequency attacks can be used in cryptography to solve cryptograms. See frequency analysis.
Bigram frequency is one approach to statistical language identification.
Some activities in logology or recreational linguistics involve bigrams. These include attempts to find English words beginning with every possible bigram, or words containing a string of repeated bigrams, such as logogogue.
Bigram frequency in the English language
The frequency of the most common letter bigrams in a large English corpus is:
th 3.56% of 1.17% io 0.83% he 3.07% ed 1.17% le 0.83% in 2.43% is 1.13% ve 0.83% er 2.05% it 1.12% co 0.79% an 1.99% al 1.09% me 0.79% re 1.85% ar 1.07% de 0.76% on 1.76% st 1.05% hi 0.76% at 1.49% to 1.05% ri 0.73% en 1.45% nt 1.04% ro 0.73% nd 1.35% ng 0.95% ic 0.70% ti 1.34% se 0.93% ne 0.69% es 1.34% ha 0.93% ea 0.69% or 1.28% as 0.87% ra 0.69% te 1.20% ou 0.87% ce 0.65%
- Digraph (orthography)
- Letter frequency
- Sørensen–Dice coefficient
- ^ Collins, Michael John (1996-06-24). "A new statistical parser based on bigram lexical dependencies". Proceedings of the 34th annual meeting on Association for Computational Linguistics -. Association for Computational Linguistics. pp. 184–191. arXiv:cmp-lg/9605012. doi:10.3115/981863.981888. S2CID 12615602. Retrieved 2018-10-09.
- ^ Cohen, Philip M. (1975). "Initial Bigrams". Word Ways. 8 (2). Retrieved 11 September 2016.
- ^ Corbin, Kyle (1989). "Double, Triple, and Quadruple Bigrams". Word Ways. 22 (3). Retrieved 11 September 2016.
- ^ "English Letter Frequency Counts: Mayzner Revisited or ETAOIN SRHLDCU". norvig.com. Retrieved 2019-10-28.
- Computational linguistics
- Natural-language understanding
- Stop words
- Text processing
- Collocation extraction
- Concept mining
- Coreference resolution
- Deep linguistic processing
- Distant reading
- Information extraction
- Named-entity recognition
- Ontology learning
- Part-of-speech tagging
- Semantic role labeling
- Semantic similarity
- Sentiment analysis
- Terminology extraction
- Text mining
- Textual entailment
- Word-sense disambiguation
- Word-sense induction
datasets and corpora
and data capture