📕 subnode [[@KGBicheno/n gram]]
in 📚 node [[n-gram]]
📓
garden/KGBicheno/Artificial Intelligence/Introduction to AI/Week 3 - Introduction/Definitions/N-Gram.md by @KGBicheno
N-gram
Go back to the [[AI Glossary]]
#seq
An ordered sequence of N words. For example, truly madly is a 2-gram. Because order is relevant, madly truly is a different 2-gram than truly madly.
| N | Name(s) for this kind of N-gram | Examples |
|---|---|---|
| 2 | bigram or 2-gram | to go, go to, eat lunch, eat dinner |
| 3 | trigram or 3-gram | ate too much, three blind mice, the bell tolls |
| 4 | 4-gram | walk in the park, dust in the wind, the boy ate lentils |
Many natural language understanding models rely on N-grams to predict the next word that the user will type or say. For example, suppose a user typed three blind. An NLU model based on trigrams would likely predict that the user will next type mice.
Contrast N-grams with bag of words, which are unordered sets of words.
📖 stoas
- public document at doc.anagora.org/n-gram
- video call at meet.jit.si/n-gram