อธิบาย Bigrams และ Trigrams

The n in n-gram is just a number. Pick n and you decide how many neighboring words each feature bundles together.

A bigram is an n-gram of size two, every adjacent pair of words. It is the most common upgrade over plain single words. 👍

From four words you get three overlapping pairs. Notice how each bigram shares one word with the next one.

A trigram takes three words at a time. It captures even more context, like is not good as one unit.

For a sentence of N words, you get N minus n plus one n-grams. So 4 words give 3 bigrams and 2 trigrams.

Each n-gram comes from a small window moving one word forward. Overlap is the point, since shared words stitch context together.

You can build any n-gram with one tidy function using zip. Change the value of n and the same code handles bigrams or trigrams.

Higher values of n hold longer phrases, so they capture context more precisely. That power comes with a cost you will meet soon.

The longer the run, the less often that exact sequence repeats. Large n-grams become rare, appearing in few documents.

Bigrams hit a sweet spot: enough context to catch not bad, common enough to repeat across texts. That balance makes them a reliable default.

You rarely use one size alone. Most pipelines combine unigrams and bigrams so the model sees both single words and their key pairs.

How many bigrams come from the sentence text is just data, which has four words?

A bigram joins two words, a trigram joins three. Bigger n holds more context but appears less often. Next you will let scikit-learn build them. ⚙️