# What this NLP guide covers

Subject: Natural Language Processing

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/nlp/#tee/about/scope/sources

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/tee/about/scope--sources/

NLP · M2 and TEE · edition 3 October 2026

**M2 portion confirmed by you:** Unit 3 from PDF slide 62 to 160; Unit 4 complete (128 slides); Unit 5 slides 1–57. Slide 62 of Unit 3 starts N-grams. Slide 57 of Unit 5 completes the Naive Bayes WSD example. Vector-space models start on slide 58 and belong to TEE here.

**M2 pattern confirmed by you:** both 2-mark questions compulsory; any two of the three 3-mark questions. Total attempted 10. Course policy gives one hour. Three newly authored, fully solved M2 practice papers use that pattern.

**TEE:** all seven units of the official AY 2026–27 syllabus. The new ZIP supplies faculty slide packs for Units 1–5 plus a regular-expressions handout. Units 6–7 are syllabus-based explanations supplemented with the standard references below; their faculty slide packs were not supplied. The official syllabus includes GloVe and spelling correction, and both are retained even though the policy’s lecture table abbreviates them.

**Previous papers found:** final exam dated 2 December 2025 and re-exam dated 9 February 2026, with answer keys, inside Semester V.zip. Programme copies were deduplicated. Every supplied question subpart is mapped to the current syllabus; no question was excluded as out of syllabus. Only wholly M2-eligible subparts appear in the M2 paper tab; mixed questions remain in the complete TEE papers.

The current syllabus lists course code 702AI0C037. The supplied policy and old papers use 702AI0C009; the policy also retains an older programme/semester header. Topic coverage follows the current official syllabus and your exact M2 cutoff, rather than treating the old header as a different course.

### Conventions that must stay visible

Course bigram answer keys count both start and end symbols in vocabulary size. Worked PYQs state and follow that convention; other exercises state their own vocabulary explicitly. A start symbol is conditioning context, so alternative textbook conventions may exclude it from prediction vocabulary and produce a different smoothed number. HMM path scores and Naive Bayes class scores are labelled as unnormalised when applicable. Any normalisation, case-folding or lemmatisation assumption is stated before counting.

### Reading references

- Your uploaded course policy, official Semester V syllabus, five NLP slide packs and regular-expressions handout.
- Your uploaded final and re-exam questions and answer keys (AY 2025–26).
- [Jurafsky & Martin · Speech and Language Processing](https://web.stanford.edu/~jurafsky/slp3/) — terminology and standard methods.
- [NLTK Book](https://www.nltk.org/book/) — corpus, tagging, parsing and information-extraction concepts.
- [Tetreault · A Corpus-Based Evaluation of Centering and Pronoun Resolution](https://aclanthology.org/J01-4003.pdf) — precise Hobbs traversal.

Examples are teaching problems unless explicitly identified by their paper and question number. Practice-paper marks indicate intended answer length, not an official forecast. Reading alone cannot guarantee a score; writing the solutions independently is what makes the procedures usable under exam time.

### TEE depth review · 10 October 2026

The core TEE route retains the faculty topics CYK/CKY, grammar conversion, interpolation and Stupid Backoff. Add-k and HMM forward/posterior calculations are available separately under Optional extensions; they are not part of the core reading or formula sequence. The two TEE timed-practice entries reproduce the supplied actual papers and are not additional unseen mock papers.

Method references: [Jurafsky and Martin: Hidden Markov Models](https://web.stanford.edu/~jurafsky/slp3/A.pdf); [Jurafsky and Martin: Constituency Parsing](https://web.stanford.edu/~jurafsky/slp3/E.pdf). New numerical inputs and worked traces are teaching examples.