# Latest and older papers: exact question map

Subject: Natural Language Processing

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/nlp/#tee/about/pyq-analysis/all-papers

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/tee/about/pyq-analysis--all-papers/

### How to use this question map

Start with the newest final and re-exam, then use older papers to practise variations. The table links each original lettered part to its complete worked answer. **Repeated questions are history, not a prediction.** A syllabus topic stays required even when no recovered paper asks it.

**Marks:** 100 marks are attempted: compulsory Q1 (20), then any four other 20-mark questions. Most recovered papers offer140 marks; NLP December2023 offers120 because the complete original ends at Q6. A mixed question retains one mark allocation; its units are not counted as separate questions. Blank individual allocations remain unknown.

### 2026-02-09 · re-exam · R26

140 marks offered; 100 attempted.

| Question [marks] | Current unit and topic | Question form | Answer / scope |
| --- | --- | --- | --- |
| **Q1a [5]** | Unit 1 · NLP pipeline | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q1a/) |
| **Q1b [5]** | Unit 1, 4 · Content and function words | mixed | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q1b/) |
| **Q1c [5]** | Unit 5 · Word2Vec and GloVe | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q1c/) |
| **Q1d [5]** | Unit 6 · Cataphora, anaphora and coreference | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q1d/) |
| **Q2a [10]** | Unit 1 · Five NLP tasks; syntax and semantics | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q2a/) |
| **Q2b [10]** | Unit 3 · Add-one bigram sentence probability | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q2b/) |
| **Q3a [10]** | Unit 4 · Top-down CFG parsing | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q3a/) |
| **Q3b [10]** | Unit 5 · Lesk with supplied glosses | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q3b/) |
| **Q4a [10]** | Unit 2 · Bag of words and three cosine similarities | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q4a/) |
| **Q4b [10]** | Unit 4 · HMM estimation and given tag-path joint probability | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q4b/) |
| **Q5a [10]** | Unit 2 · Explain and illustrate TF-IDF | mixed | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q5a/) |
| **Q5b [10]** | Unit 5 · Co-occurrence, cosine and normalized plot | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q5b/) · Target/context normalization and sentence-presence convention stated; alternative target lemmatization has a separately calculated result. |
| **Q6a [10]** | Unit 5, 6 · Pronoun-resolution methods; homonymy and synonymy | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q6a/) |
| **Q6b [10]** | Unit 6 · Hobbs antecedent traversal | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q6b/) · Hobbs requires a parse and compatibility assumptions. The assumed parse and traversal order are disclosed. |
| **Q7a [10]** | Unit 7 · Information-retrieval stages | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q7a/) |
| **Q7b [10]** | Unit 2, 3 · Gutenberg and text normalization | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/reexam-2026--reexam-2026-q7b/) |

**Original offered marks by mapped topic, grouped without double counting:** Unit 1: 2 parts, 15 known marks; Unit 1, 4: 1 parts, 5 known marks; Unit 5: 3 parts, 25 known marks; Unit 6: 2 parts, 15 known marks; Unit 3: 1 parts, 10 known marks; Unit 4: 2 parts, 20 known marks; Unit 2: 2 parts, 20 known marks; Unit 5, 6: 1 parts, 10 known marks; Unit 7: 1 parts, 10 known marks; Unit 2, 3: 1 parts, 10 known marks. Joint-unit buckets remain joint; no invented split is assigned. These original-paper totals include any legacy tasks explicitly marked above; they are not a current-syllabus marks blueprint.

### 2025-12-02 · final · F25

140 marks offered; 100 attempted.

| Question [marks] | Current unit and topic | Question form | Answer / scope |
| --- | --- | --- | --- |
| **Q1a [5]** | Unit 1 · NLU versus NLG | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q1a/) |
| **Q1b [5]** | Unit 4 · Chunking, chinking and POS | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q1b/) |
| **Q1c [5]** | Unit 5 · Hyponymy/hypernymy and WSD | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q1c/) |
| **Q1d [5]** | Unit 6 · Pragmatic and discourse analysis | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q1d/) |
| **Q2a [10]** | Unit 1, 4 · Morpheme/lexeme and NER applications | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q2a/) |
| **Q2b [10]** | Unit 3 · Add-one bigram sentence probability | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q2b/) |
| **Q3a [10]** | Unit 1, 4 · Ambiguities and bottom-up CFG parse | mixed | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q3a/) |
| **Q3b [10]** | Unit 5 · Co-occurrence, cosine and vector plot | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q3b/) |
| **Q4a [10]** | Unit 2 · Three preprocessing techniques | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q4a/) |
| **Q4b [10]** | Unit 4 · HMM estimation and given tag-path joint probability | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q4b/) |
| **Q5a [10]** | Unit 2 · TF-IDF matrix | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q5a/) |
| **Q5b [10]** | Unit 5 · Naive Bayes word-sense disambiguation | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q5b/) · Fixed vocabulary/OOV treatment is stated; source-key OOV smoothing differs. Both conventions and their consequence are explained. |
| **Q6a [10]** | Unit 5, 6 · Coreference/pronouns; CBOW and Skip-gram | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q6a/) |
| **Q6b [10]** | Unit 6 · Hobbs antecedent traversal | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q6b/) · Hobbs requires a parse and compatibility assumptions. The assumed parse and traversal order are disclosed. |
| **Q7a [10]** | Unit 7 · Document-classification pipeline | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q7a/) |
| **Q7b [10]** | Unit 2, 3 · Corpus structures and Jaccard example | mixed | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/final-2025--final-2025-q7b/) |

**Original offered marks by mapped topic, grouped without double counting:** Unit 1: 1 parts, 5 known marks; Unit 4: 2 parts, 15 known marks; Unit 5: 3 parts, 25 known marks; Unit 6: 2 parts, 15 known marks; Unit 1, 4: 2 parts, 20 known marks; Unit 3: 1 parts, 10 known marks; Unit 2: 2 parts, 20 known marks; Unit 5, 6: 1 parts, 10 known marks; Unit 7: 1 parts, 10 known marks; Unit 2, 3: 1 parts, 10 known marks. Joint-unit buckets remain joint; no invented split is assigned. These original-paper totals include any legacy tasks explicitly marked above; they are not a current-syllabus marks blueprint.

### 2025-02-21 · re-exam · R25

140 marks offered; 100 attempted. Q1 total20 is printed, but the four individual part allocations are blank. Do not infer5marks as if printed. Sixth PDF page is blank.

| Question [marks] | Current unit and topic | Question form | Answer / scope |
| --- | --- | --- | --- |
| **Q1a [not printed individually]** | Unit 2 · BoW and one-hot encoding | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q1a/) |
| **Q1b [not printed individually]** | Unit 5 · Lesk WSD | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q1b/) |
| **Q1c [not printed individually]** | Unit 4 · Syntax parsing approaches | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q1c/) |
| **Q1d [not printed individually]** | Unit 1, 7 · NLU versus NLG and applications | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q1d/) |
| **Q2a [10]** | Unit 2 · TF–IDF cosine after a fixed stoplist | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q2a/) |
| **Q2b [10]** | Unit 7 · Weather Naive Bayes | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q2b/) |
| **Q3a [5]** | Unit 6 · Coreference and anaphora | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q3a/) |
| **Q3b [5]** | Unit 6 · Cohesion, coherence and discourse markers | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q3b/) |
| **Q3c [10]** | Unit 7 · Question answering system | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q3c/) |
| **Q4a [5]** | Unit 1 · NLP pipeline | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q4a/) |
| **Q4b [5]** | Unit 1 · Morphology and syntax | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q4b/) |
| **Q4c [10]** | Unit 3 · Most probable bigram continuations | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q4c/) |
| **Q5a [6]** | Unit 2 · Preprocess the NLP passage | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q5a/) |
| **Q5b [4]** | Unit 2 · Stemming and lemmatization tools | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q5b/) |
| **Q5c [10]** | Unit 6 · Hobbs on John and Mary | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q5c/) |
| **Q6a [10]** | Unit 3 · Corpora and acquisition | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q6a/) |
| **Q6b [10]** | Unit 4 · HMM components and estimates | mixed | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q6b/) |
| **Q7a [5]** | Unit 4 · Word classes, chunking and chinking | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q7a/) |
| **Q7b [5]** | Unit 4 · Named entity recognition | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q7b/) |
| **Q7c [10]** | Unit 5 · Co-occurrence with inconsistent requested comparisons | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r25--q7c/) · Source defect: follow-up names belong to another corpus. Exact supported matrix is complete; unsupported parts are explicitly unresolved from the original data. |

**Original offered marks by mapped topic, grouped without double counting:** Unit 2: 4 parts, 20 known marks + 1 parts with no individual mark printed; Unit 5: 2 parts, 10 known marks + 1 parts with no individual mark printed; Unit 4: 4 parts, 20 known marks + 1 parts with no individual mark printed; Unit 1, 7: 1 parts, 0 known marks + 1 parts with no individual mark printed; Unit 7: 2 parts, 20 known marks; Unit 6: 3 parts, 20 known marks; Unit 1: 2 parts, 10 known marks; Unit 3: 2 parts, 20 known marks. Joint-unit buckets remain joint; no invented split is assigned. These original-paper totals include any legacy tasks explicitly marked above; they are not a current-syllabus marks blueprint.

### 2024-12-03 · final · F24

140 marks offered; 100 attempted. Four Q1 part allocations are blank; their group totals20. Other printed lettered allocations determine each 20-mark main question.

| Question [marks] | Current unit and topic | Question form | Answer / scope |
| --- | --- | --- | --- |
| **Q1a [not printed individually]** | Unit 2, 7 · TF–IDF in retrieval | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q1a/) |
| **Q1b [not printed individually]** | Unit 4 · NER and approaches | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q1b/) |
| **Q1c [not printed individually]** | Unit 5 · Skip-gram and CBOW | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q1c/) |
| **Q1d [not printed individually]** | Unit 6 · Pragmatics and discourse | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q1d/) |
| **Q2a [6]** | Unit 2, 4 · Six preprocessing operations | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q2a/) |
| **Q2b [4]** | Unit 2, 5 · Lexical similarity and cosine | mixed | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q2b/) |
| **Q2c [10]** | Unit 7 · Animal categorical Naive Bayes | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q2c/) |
| **Q3a [10]** | Unit 6 · Hobbs and ambiguous professor–student reference | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q3a/) · Pronoun referent is ambiguous without extra gender/discourse information; structural algorithm preference is separated from semantic interpretation. |
| **Q3b [10]** | Unit 7 · Classification and sentiment in industry | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q3b/) |
| **Q4a [10]** | Unit 4 · Top-down syntax tree | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q4a/) |
| **Q4b [10]** | Unit 3 · Data acquisition and four augmentation methods | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q4b/) |
| **Q5a [10]** | Unit 2 · Feature engineering, BoW and Euclidean | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q5a/) |
| **Q5b [5]** | Unit 5 · WSD approaches and uses | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q5b/) |
| **Q5c [5]** | Unit 6 · Discourse analysis of a conversation | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q5c/) |
| **Q6a [10]** | Unit 3 · Henry bigram continuations | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q6a/) · Case is retained as printed: Do and do are distinct. The corpus is not silently grammatically corrected. |
| **Q6b [5]** | Unit 1 · Five linguistic levels | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q6b/) |
| **Q6c [5]** | Unit 1 · NLU and NLG working together | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q6c/) |
| **Q7a [10]** | Unit 4 · POS, chunks and boundary-aware HMM | mixed | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q7a/) · Printed tags are used as training data; START and END are explicitly required and included. |
| **Q7b [10]** | Unit 5 · Co-occurrence, dot products and plot | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f24--q7b/) |

**Original offered marks by mapped topic, grouped without double counting:** Unit 2, 7: 1 parts, 0 known marks + 1 parts with no individual mark printed; Unit 4: 3 parts, 20 known marks + 1 parts with no individual mark printed; Unit 5: 3 parts, 15 known marks + 1 parts with no individual mark printed; Unit 6: 3 parts, 15 known marks + 1 parts with no individual mark printed; Unit 2, 4: 1 parts, 6 known marks; Unit 2, 5: 1 parts, 4 known marks; Unit 7: 2 parts, 20 known marks; Unit 3: 2 parts, 20 known marks; Unit 2: 1 parts, 10 known marks; Unit 1: 2 parts, 10 known marks. Joint-unit buckets remain joint; no invented split is assigned. These original-paper totals include any legacy tasks explicitly marked above; they are not a current-syllabus marks blueprint.

### 2024-02-12 · re-exam · R24

140 marks offered; 100 attempted.

| Question [marks] | Current unit and topic | Question form | Answer / scope |
| --- | --- | --- | --- |
| **Q1a [5]** | Unit 6 · Anaphora versus cataphora | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q1a/) |
| **Q1b [5]** | Unit 3 · Five corpora | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q1b/) |
| **Q1c [5]** | Unit 1 · Linguistic foundations | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q1c/) |
| **Q1d [5]** | Unit 3 · Five regex operators | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q1d/) |
| **Q2a [10]** | Unit 3 · Wordlist puzzle and lexical resource | mixed | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q2a/) · The dictionary is unspecified; an exact number of valid words would be fabricated. |
| **Q2b [10]** | Unit 1, 7 · NLP applications and limitations | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q2b/) |
| **Q3a [10]** | Unit 4 · Top-down restaurant parse | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q3a/) · Original CFG is incomplete for the given bare-plural object. Exact failure and explicit repaired derivation are both shown. |
| **Q3b [10]** | Unit 2 · Preprocessing pipeline | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q3b/) |
| **Q4a [10]** | Unit 5 · Embeddings and Skip-gram | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q4a/) |
| **Q4b [10]** | Unit 2, 4 · POS, NER, chunking and TTR | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q4b/) |
| **Q5a [10]** | Unit 5 · Lexical and vector semantics | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q5a/) |
| **Q5b [10]** | Unit 7 · Classification label structures | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q5b/) |
| **Q6a [10]** | Unit 2, 7 · TF–IDF in document representation | mixed | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q6a/) · The prompt requests an example but supplies no corpus. Numbers in the explanation are authored illustrations, not original numerical givens. |
| **Q6b [10]** | Unit 4 · HMM tables and path | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q6b/) |
| **Q7a [10]** | Unit 7 · Healthcare and finance applications | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q7a/) |
| **Q7b [10]** | Unit 7 · Naive Bayes sentiment | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r24--q7b/) |

**Original offered marks by mapped topic, grouped without double counting:** Unit 6: 1 parts, 5 known marks; Unit 3: 3 parts, 20 known marks; Unit 1: 1 parts, 5 known marks; Unit 1, 7: 1 parts, 10 known marks; Unit 4: 2 parts, 20 known marks; Unit 2: 1 parts, 10 known marks; Unit 5: 2 parts, 20 known marks; Unit 2, 4: 1 parts, 10 known marks; Unit 7: 3 parts, 30 known marks; Unit 2, 7: 1 parts, 10 known marks. Joint-unit buckets remain joint; no invented split is assigned. These original-paper totals include any legacy tasks explicitly marked above; they are not a current-syllabus marks blueprint.

### 2023-12-01 · final · F23

120 marks offered; 100 attempted. Original has six main questions on three pages: Q1 compulsory and any four of Q2–Q6. Offered total 120, attempted 100.

| Question [marks] | Current unit and topic | Question form | Answer / scope |
| --- | --- | --- | --- |
| **Q1a [5]** | Unit 5, 6 · WSD and pronoun resolution | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q1a/) |
| **Q1b [5]** | Unit 5 · WordNet and its hierarchy | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q1b/) |
| **Q1c [5]** | Unit 2, 7 · Sentiment feature extraction | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q1c/) |
| **Q1d [5]** | Unit 7 · Binary, multiclass and multilabel | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q1d/) |
| **Q2a [10]** | Unit 3 · Text corpus structure and applications | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q2a/) |
| **Q2b [10]** | Unit 5 · Lesk versus Walker and bank disambiguation | mixed | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q2b/) · The original gives a sentence but no thesaurus/category mapping, so a unique numerical score cannot be recovered. |
| **Q3a [10]** | Unit 6 · Hobbs algorithm and limitations | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q3a/) |
| **Q3b [10]** | Unit 2 · Stemming and lemmatization | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q3b/) |
| **Q4a [10]** | Unit 5 · Lexical and vector semantics | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q4a/) |
| **Q4b [10]** | Unit 2 · Three-document TF–IDF | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q4b/) |
| **Q5a [10]** | Unit 5 · Word embeddings and CBOW | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q5a/) |
| **Q5b [10]** | Unit 4 · HMM tables and given path | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q5b/) |
| **Q6a [10]** | Unit 7 · Text classification pipeline | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q6a/) |
| **Q6b [10]** | Unit 2, 7 · VSM, TF and IDF in retrieval | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f23--q6b/) |

**Original offered marks by mapped topic, grouped without double counting:** Unit 5, 6: 1 parts, 5 known marks; Unit 5: 4 parts, 35 known marks; Unit 2, 7: 2 parts, 15 known marks; Unit 7: 2 parts, 15 known marks; Unit 3: 1 parts, 10 known marks; Unit 6: 1 parts, 10 known marks; Unit 2: 2 parts, 20 known marks; Unit 4: 1 parts, 10 known marks. Joint-unit buckets remain joint; no invented split is assigned. These original-paper totals include any legacy tasks explicitly marked above; they are not a current-syllabus marks blueprint.

### 2023-01-30 · re-exam · R23

140 marks offered; 100 attempted. Q1 header is unclear/conflicting [14/20], but all four parts print 5 marks each; sum=20. Main paper is 100 attempted marks.

| Question [marks] | Current unit and topic | Question form | Answer / scope |
| --- | --- | --- | --- |
| **Q1a [5]** | Unit 2 · Normalization changes vocabulary | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q1a/) |
| **Q1b [5]** | Unit 3 · Good corpus characteristics | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q1b/) |
| **Q1c [5]** | Unit 5 · Word2Vec versus Doc2Vec | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q1c/) · legacy named-method extension; not separately named in current syllabus. Optional historical extension; not a separately prescribed current-syllabus method. |
| **Q1d [5]** | Unit 1, 4 · Ambiguity in NLP | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q1d/) |
| **Q2a [10]** | Unit 2 · Normalize the Tanisha–Simran passage | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q2a/) · Stoplist and stemming algorithm are choices, not supplied numerical givens; both are stated. |
| **Q2b [10]** | Unit 2, 5 · Text vectorization alternatives | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q2b/) |
| **Q3a [10]** | Unit 1, 5, 6 · Semantics, pragmatics and two ambiguities | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q3a/) |
| **Q3b [10]** | Unit 7 · Naive Bayes text classifier | mixed | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q3b/) · Chinese corpus is an authored/reused teaching illustration here; it is actual given data in F22 Q5b, not this prompt. |
| **Q4a [10]** | Unit 2 · Jaccard, cosine and Euclidean without preprocessing | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q4a/) |
| **Q4b [10]** | Unit 5 · CBOW and Skip-gram | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q4b/) |
| **Q5a [10]** | Unit 3 · Password regex | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q5a/) |
| **Q5b [10]** | Unit 7 · Two NLP applications | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q5b/) |
| **Q6a [10]** | Unit 6 · Mention-pair model | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q6a/) |
| **Q6b [10]** | Unit 7 · Classification evaluation including ROC/AUC | mixed | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q6b/) |
| **Q7a [10]** | Unit 4 · Organization and facility mentions | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q7a/) · Boundary and generic-mention decisions depend on the provided ACE annotation convention; the answer distinguishes them from a named-only interpretation. |
| **Q7b [10]** | Unit 4 · Tagging and two complete parse trees | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-r23--q7b/) |

**Original offered marks by mapped topic, grouped without double counting:** Unit 2: 3 parts, 25 known marks; Unit 3: 2 parts, 15 known marks; Unit 5: 2 parts, 15 known marks; Unit 1, 4: 1 parts, 5 known marks; Unit 2, 5: 1 parts, 10 known marks; Unit 1, 5, 6: 1 parts, 10 known marks; Unit 7: 3 parts, 30 known marks; Unit 6: 1 parts, 10 known marks; Unit 4: 2 parts, 20 known marks. Joint-unit buckets remain joint; no invented split is assigned. These original-paper totals include any legacy tasks explicitly marked above; they are not a current-syllabus marks blueprint.

**Current-syllabus qualification:** Q1c: historical named-method extension, excluded from current-only priority totals. The original whole-part marks remain visible so the paper is faithfully represented.

### 2022-11-22 · final · F22

140 marks offered; 100 attempted.

| Question [marks] | Current unit and topic | Question form | Answer / scope |
| --- | --- | --- | --- |
| **Q1a [5]** | Unit 1 · Need and difficulties of NLP | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q1a/) |
| **Q1b [5]** | Unit 1 · Levels of language analysis | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q1b/) |
| **Q1c [5]** | Unit 7 · Medical chatbot | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q1c/) |
| **Q1d [5]** | Unit 7 · Sentiment with logistic regression | mixed | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q1d/) |
| **Q2a [10]** | Unit 5 · CBOW and Skip-gram architecture | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q2a/) |
| **Q2b [10]** | Unit 3 · Seven regex constructions | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q2b/) |
| **Q3a [10]** | Unit 2 · Text preprocessing | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q3a/) |
| **Q3b [10]** | Unit 4 · Penn Treebank tagging | case | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q3b/) · Context-sensitive tags and possessive tokenization are explicitly stated; reasonable corpus guidelines can differ on noun adjuncts. |
| **Q4a [10]** | Unit 6 · Mention-pair coreference | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q4a/) |
| **Q4b [10]** | Unit 7 · Text classification pipeline | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q4b/) |
| **Q5a [10]** | Unit 3 · Corpora and structure | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q5a/) |
| **Q5b [10]** | Unit 7 · Multinomial Naive Bayes | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q5b/) |
| **Q6a [10]** | Unit 5 · GloVe | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q6a/) |
| **Q6b [10]** | Unit 2 · BoW and TF–IDF movie corpus | numerical | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q6b/) |
| **Q7a [10]** | Unit 4, 7 · Information extraction, chunking and NER | theory | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q7a/) |
| **Q7b [10]** | Unit 5 · Distributional hypothesis and cosine | mixed | [Read question + answer](https://deep-learning-study-room.vercel.app/read/nlp/tee/papers/legacy-f22--q7b/) |

**Original offered marks by mapped topic, grouped without double counting:** Unit 1: 2 parts, 10 known marks; Unit 7: 4 parts, 30 known marks; Unit 5: 3 parts, 30 known marks; Unit 3: 2 parts, 20 known marks; Unit 2: 2 parts, 20 known marks; Unit 4: 1 parts, 10 known marks; Unit 6: 1 parts, 10 known marks; Unit 4, 7: 1 parts, 10 known marks. Joint-unit buckets remain joint; no invented split is assigned. These original-paper totals include any legacy tasks explicitly marked above; they are not a current-syllabus marks blueprint.

### Priority without skipping the syllabus

The latest papers show the form and depth of questions, while the official current syllabus sets the learning scope. Authored drills, explanatory numbers and optional extensions are not counted as past questions. Source assumptions and ambiguous data are stated in their answers.