countngram

Counting n-grams is a fundamental technique in natural language processing and computational linguistics used to analyze and model text data. An n-gram is a contiguous sequence of n items—typically words or characters—extracted from a given text. For example, in the phrase "natural language processing," the bigrams (2-grams) are "natural language" and "language processing," while the trigrams (3-grams) are "natural language processing."

The process of counting n-grams involves sliding a window of size n over the text and tallying each occurrence of these sequences. This count provides insight into common patterns, phrase frequencies, and syntactic or semantic relationships within the corpus. N-gram counts are vital for applications such as language modeling, text prediction, and information retrieval.

In language modeling, n-gram frequency distributions serve as the basis for probabilistic models that predict the likelihood of subsequent words based on historical context. These models rely on the assumption that the probability of a word depends primarily on the n-1 previous words, an approach known as the Markov assumption. Counting n-grams efficiently can be achieved through data structures like hash tables or prefix trees, which support rapid frequency retrieval.

Overall, n-gram counting provides essential statistical features for various NLP tasks, facilitating a better understanding of language structure and improving the performance of language processing systems. Its simplicity and effectiveness make it a popular method despite limitations in handling long-range dependencies and context beyond the specified n-gram size.