# Recognise the question and start correctly

Subject: Natural Language Processing

Exam: M2

Reader: https://deep-learning-study-room.vercel.app/nlp/#m2/revision/m2-revision/method

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/m2/revision/m2-revision--method/

M2 · quick revision after completing the lessons

| Question clue | Use this method | First thing to write |
| --- | --- | --- |
| “next word”, “sentence probability” | N-gram model | List the needed word transitions. |
| “unseen pair”, “Laplace” | Add-one smoothing | State V and count each history. |
| “perplexity” | Reciprocal Nth root | State P(test sequence) and N. |
| “transition/emission matrix” | HMM count estimation | Separate tag-to-tag counts from tag-to-word counts. |
| “given tag sequence” | HMM joint path | Initial × emission × transition × emission … |
| “best tag sequence” | Viterbi or enumerate tiny paths | Initialise one score per tag; retain predecessor maxima. |
| “gloss overlap” | Simplified Lesk | Preprocess context and glosses consistently. |
| “classify word sense with counts” | Naive Bayes WSD | Priors from examples, likelihoods from tokens. |
| “parse tree” | Given CFG + chosen parser | Show grammar expansions or stack reductions. |

### Errors that change the answer

1. Dividing a conditional count by the whole corpus instead of its history count. 2. Adding 1 to the numerator without adding |V| to the denominator. 3. Forgetting the end transition. 4. Counting repeated words as new vocabulary types. 5. Using P(tag|word) in place of P(word|tag). 6. Calling an unnormalised score a posterior. 7. Silently mixing case, lemmatisation or boundary conventions.

### Short theory answer format

For 2 marks: give the exact definition and one specific example or distinction. For 3 marks: add the mechanism or two ordered steps; include a small diagram/table where it carries information. The actual allocation is examiner-dependent, so these are writing guides, not an official marking scheme.