Briefly describe the N gram model in NLP

The N-gram model is a probabilistic model commonly used in natural language processing (NLP) for modeling sequences of words or tokens in text data. It is based on the concept of considering sequences of N consecutive words or tokens, known as N-grams, and estimating the probabilities of observing specific N-grams in a given corpus of text.

Here's a brief description of the N-gram model:

1. Definition:

   • An N-gram is a contiguous sequence of N items from a given sequence of text, where the items can be words, characters, or other tokens. For example, a bigram is a sequence of two words, a trigram is a sequence of three words, and so on.
   • The N-gram model aims to model the probability distribution of observing specific N-grams in a corpus of text, which can then be used for various NLP tasks such as language modeling, text generation, and information retrieval.

2. Assumptions:

   • The N-gram model makes the Markov assumption, which states that the probability of observing a word or token in a sequence depends only on the preceding N-1 words or tokens. In other words, the probability of the current item depends only on the previous N-1 items.

3. Estimation:

   • The N-gram probabilities are estimated from a corpus of text by counting the occurrences of each N-gram and calculating the conditional probabilities of observing specific items given the preceding N-1 items.
   • For example, in a bigram model (N=2), the probability of observing a word given the preceding word is estimated by counting the occurrences of each word pair (bigram) and dividing by the total count of the preceding word.

4. Applications:

   • The N-gram model is widely used in various NLP tasks, including language modeling (predicting the next word in a sequence), part-of-speech tagging, machine translation, text summarization, and speech recognition.
   • It serves as a foundation for more advanced models such as hidden Markov models (HMMs), conditional random fields (CRFs), and neural network-based language models (e.g., LSTMs, transformers).

Overall, the N-gram model provides a simple but effective way to capture local dependencies and statistical properties of text data, making it a fundamental tool in NLP research and applications.