# From a corpus to sentence probabilities

Subject: Natural Language Processing

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/nlp/#tee/read/m2-count-to-probability

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/tee/read-chapter/m2-count-to-probability/

Unit 3, pages 97–120 and appendix

1. [Unigram estimation and complete worked examples](https://deep-learning-study-room.vercel.app/read/nlp/tee/read/m2-count-to-probability--unigram-estimation/)
2. [Bigram counts: build the table before multiplying](https://deep-learning-study-room.vercel.app/read/nlp/tee/read/m2-count-to-probability--bigram-count-table/)
3. [Trigram estimates: two-word history, not two denominators](https://deep-learning-study-room.vercel.app/read/nlp/tee/read/m2-count-to-probability--trigram-estimation/)
4. [Class-style count-table questions and partial-table traps](https://deep-learning-study-room.vercel.app/read/nlp/tee/read/m2-count-to-probability--class-count-table-questions/)