What is the bigram model in NLP

In natural language processing (NLP), the bigram model is a statistical language model that models the probability of a word given its preceding word. Specifically, it assumes that the probability of a word depends only on the preceding word. This is in contrast to unigram models, which only consider the probability of individual words occurring independently of each other.

Here's how the bigram model works:

1. Definition: A bigram is an ordered pair of words. In the bigram model, we calculate the probability of a word given its preceding word. Mathematically, this is expressed as P(wi | wi-1), where wi is the current word and wi-1 is the preceding word.

2. Training: To build a bigram model, we analyze a corpus of text data and count the occurrences of each word pair (bigram). We then calculate the conditional probabilities of each word given its preceding word. This is often done by dividing the count of the word pair by the count of the preceding word.

3. Prediction: During the prediction phase, given a sequence of words, we use the bigram probabilities to estimate the likelihood of the next word following each word in the sequence. We can then choose the word with the highest probability as the predicted next word.

4. Smoothing: Since the bigram model may encounter word pairs that were not present in the training data, smoothing techniques such as Laplace smoothing or Good-Turing smoothing are often applied to handle unseen word combinations.

The bigram model captures some local dependencies in language but may not capture long-range dependencies or context very well, as it only considers the previous word. However, it's a simple and computationally efficient approach that has been used in various NLP tasks such as text generation, part-of-speech tagging, and machine translation. Additionally, it serves as a building block for more sophisticated language models, such as the trigram model (considering the two preceding words) and higher-order n-gram models.