N-grams are contiguous sequences of tokens extracted from text where ‘N’ denotes the number of tokens. In the most common instance, tokens are words (although for some applications such as automated spelling corrections, individual letters can also serve as tokens) and thus n-grams denote short sequences of words extracted from sentences. Unigrams (or 1-grams) are the most basic n-grams and represent single words but bi-grams, tri-grams and higher order n-grams can consist of sequences of two, three or more words. As an example, if our sentence is “The quality of mercy is not strained” we can extract the following n-grams from it (after a bit of pre-processing to turn words into lowercase):

1-grams: ['the', 'quality', 'of', 'mercy', 'is', 'not', 
2-grams: ['the quality', 'quality of', 'of mercy', 
            'mercy is', 'is not', 'not strained']
3-grams: ['the quality of', 'quality of mercy', 
            'of mercy is', 'mercy is not', 'is not strained']

When extracted from a large corpus of texts, n-gram frequencies can be invaluable for constructing probabilistic models of language and are widely used in computational natural language processing and corpus linguistics. As such, a large scale n-gram database is the basic building block for the computational analysis of EEBO-TCP’s vast archive. Even at the simple level of relative frequencies plotted over time, n-grams can help us explore a wide range of research questions ranging from the dissipation of literary influence, the evolution of style and genre, to the gradual standardization of orthography within quantitative frameworks and at scales not possible before. We hope that these n-gram databases will help us answer and, more importantly, frame new perspectives and questions drawing on a vast body of early English print.