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Latest and older papers: exact question map

All recovered TEE papers · unit, topic and marks map

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
Q1b [5]	Unit 1, 4 · Content and function words	mixed	Read question + answer
Q1c [5]	Unit 5 · Word2Vec and GloVe	theory	Read question + answer
Q1d [5]	Unit 6 · Cataphora, anaphora and coreference	theory	Read question + answer
Q2a [10]	Unit 1 · Five NLP tasks; syntax and semantics	theory	Read question + answer
Q2b [10]	Unit 3 · Add-one bigram sentence probability	numerical	Read question + answer
Q3a [10]	Unit 4 · Top-down CFG parsing	case	Read question + answer
Q3b [10]	Unit 5 · Lesk with supplied glosses	numerical	Read question + answer
Q4a [10]	Unit 2 · Bag of words and three cosine similarities	numerical	Read question + answer
Q4b [10]	Unit 4 · HMM estimation and given tag-path joint probability	numerical	Read question + answer
Q5a [10]	Unit 2 · Explain and illustrate TF-IDF	mixed	Read question + answer
Q5b [10]	Unit 5 · Co-occurrence, cosine and normalized plot	numerical	Read question + answer · 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
Q6b [10]	Unit 6 · Hobbs antecedent traversal	case	Read question + answer · 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
Q7b [10]	Unit 2, 3 · Gutenberg and text normalization	theory	Read question + answer

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
Q1b [5]	Unit 4 · Chunking, chinking and POS	theory	Read question + answer
Q1c [5]	Unit 5 · Hyponymy/hypernymy and WSD	theory	Read question + answer
Q1d [5]	Unit 6 · Pragmatic and discourse analysis	theory	Read question + answer
Q2a [10]	Unit 1, 4 · Morpheme/lexeme and NER applications	theory	Read question + answer
Q2b [10]	Unit 3 · Add-one bigram sentence probability	numerical	Read question + answer
Q3a [10]	Unit 1, 4 · Ambiguities and bottom-up CFG parse	mixed	Read question + answer
Q3b [10]	Unit 5 · Co-occurrence, cosine and vector plot	numerical	Read question + answer
Q4a [10]	Unit 2 · Three preprocessing techniques	theory	Read question + answer
Q4b [10]	Unit 4 · HMM estimation and given tag-path joint probability	numerical	Read question + answer
Q5a [10]	Unit 2 · TF-IDF matrix	numerical	Read question + answer
Q5b [10]	Unit 5 · Naive Bayes word-sense disambiguation	numerical	Read question + answer · 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
Q6b [10]	Unit 6 · Hobbs antecedent traversal	case	Read question + answer · 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
Q7b [10]	Unit 2, 3 · Corpus structures and Jaccard example	mixed	Read question + answer

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
Q1b [not printed individually]	Unit 5 · Lesk WSD	theory	Read question + answer
Q1c [not printed individually]	Unit 4 · Syntax parsing approaches	theory	Read question + answer
Q1d [not printed individually]	Unit 1, 7 · NLU versus NLG and applications	theory	Read question + answer
Q2a [10]	Unit 2 · TF–IDF cosine after a fixed stoplist	numerical	Read question + answer
Q2b [10]	Unit 7 · Weather Naive Bayes	numerical	Read question + answer
Q3a [5]	Unit 6 · Coreference and anaphora	theory	Read question + answer
Q3b [5]	Unit 6 · Cohesion, coherence and discourse markers	theory	Read question + answer
Q3c [10]	Unit 7 · Question answering system	theory	Read question + answer
Q4a [5]	Unit 1 · NLP pipeline	theory	Read question + answer
Q4b [5]	Unit 1 · Morphology and syntax	theory	Read question + answer
Q4c [10]	Unit 3 · Most probable bigram continuations	numerical	Read question + answer
Q5a [6]	Unit 2 · Preprocess the NLP passage	case	Read question + answer
Q5b [4]	Unit 2 · Stemming and lemmatization tools	theory	Read question + answer
Q5c [10]	Unit 6 · Hobbs on John and Mary	case	Read question + answer
Q6a [10]	Unit 3 · Corpora and acquisition	theory	Read question + answer
Q6b [10]	Unit 4 · HMM components and estimates	mixed	Read question + answer
Q7a [5]	Unit 4 · Word classes, chunking and chinking	theory	Read question + answer
Q7b [5]	Unit 4 · Named entity recognition	theory	Read question + answer
Q7c [10]	Unit 5 · Co-occurrence with inconsistent requested comparisons	numerical	Read question + answer · 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
Q1b [not printed individually]	Unit 4 · NER and approaches	theory	Read question + answer
Q1c [not printed individually]	Unit 5 · Skip-gram and CBOW	theory	Read question + answer
Q1d [not printed individually]	Unit 6 · Pragmatics and discourse	theory	Read question + answer
Q2a [6]	Unit 2, 4 · Six preprocessing operations	theory	Read question + answer
Q2b [4]	Unit 2, 5 · Lexical similarity and cosine	mixed	Read question + answer
Q2c [10]	Unit 7 · Animal categorical Naive Bayes	numerical	Read question + answer
Q3a [10]	Unit 6 · Hobbs and ambiguous professor–student reference	case	Read question + answer · 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
Q4a [10]	Unit 4 · Top-down syntax tree	case	Read question + answer
Q4b [10]	Unit 3 · Data acquisition and four augmentation methods	theory	Read question + answer
Q5a [10]	Unit 2 · Feature engineering, BoW and Euclidean	numerical	Read question + answer
Q5b [5]	Unit 5 · WSD approaches and uses	theory	Read question + answer
Q5c [5]	Unit 6 · Discourse analysis of a conversation	case	Read question + answer
Q6a [10]	Unit 3 · Henry bigram continuations	numerical	Read question + answer · 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
Q6c [5]	Unit 1 · NLU and NLG working together	theory	Read question + answer
Q7a [10]	Unit 4 · POS, chunks and boundary-aware HMM	mixed	Read question + answer · 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

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
Q1b [5]	Unit 3 · Five corpora	theory	Read question + answer
Q1c [5]	Unit 1 · Linguistic foundations	theory	Read question + answer
Q1d [5]	Unit 3 · Five regex operators	theory	Read question + answer
Q2a [10]	Unit 3 · Wordlist puzzle and lexical resource	mixed	Read question + answer · 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
Q3a [10]	Unit 4 · Top-down restaurant parse	case	Read question + answer · 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
Q4a [10]	Unit 5 · Embeddings and Skip-gram	theory	Read question + answer
Q4b [10]	Unit 2, 4 · POS, NER, chunking and TTR	theory	Read question + answer
Q5a [10]	Unit 5 · Lexical and vector semantics	theory	Read question + answer
Q5b [10]	Unit 7 · Classification label structures	theory	Read question + answer
Q6a [10]	Unit 2, 7 · TF–IDF in document representation	mixed	Read question + answer · 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
Q7a [10]	Unit 7 · Healthcare and finance applications	theory	Read question + answer
Q7b [10]	Unit 7 · Naive Bayes sentiment	numerical	Read question + answer

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
Q1b [5]	Unit 5 · WordNet and its hierarchy	theory	Read question + answer
Q1c [5]	Unit 2, 7 · Sentiment feature extraction	numerical	Read question + answer
Q1d [5]	Unit 7 · Binary, multiclass and multilabel	theory	Read question + answer
Q2a [10]	Unit 3 · Text corpus structure and applications	theory	Read question + answer
Q2b [10]	Unit 5 · Lesk versus Walker and bank disambiguation	mixed	Read question + answer · 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
Q3b [10]	Unit 2 · Stemming and lemmatization	theory	Read question + answer
Q4a [10]	Unit 5 · Lexical and vector semantics	theory	Read question + answer
Q4b [10]	Unit 2 · Three-document TF–IDF	numerical	Read question + answer
Q5a [10]	Unit 5 · Word embeddings and CBOW	theory	Read question + answer
Q5b [10]	Unit 4 · HMM tables and given path	numerical	Read question + answer
Q6a [10]	Unit 7 · Text classification pipeline	theory	Read question + answer
Q6b [10]	Unit 2, 7 · VSM, TF and IDF in retrieval	theory	Read question + answer

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
Q1b [5]	Unit 3 · Good corpus characteristics	theory	Read question + answer
Q1c [5]	Unit 5 · Word2Vec versus Doc2Vec	theory	Read question + answer · 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
Q2a [10]	Unit 2 · Normalize the Tanisha–Simran passage	case	Read question + answer · 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
Q3a [10]	Unit 1, 5, 6 · Semantics, pragmatics and two ambiguities	theory	Read question + answer
Q3b [10]	Unit 7 · Naive Bayes text classifier	mixed	Read question + answer · 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
Q4b [10]	Unit 5 · CBOW and Skip-gram	theory	Read question + answer
Q5a [10]	Unit 3 · Password regex	case	Read question + answer
Q5b [10]	Unit 7 · Two NLP applications	theory	Read question + answer
Q6a [10]	Unit 6 · Mention-pair model	theory	Read question + answer
Q6b [10]	Unit 7 · Classification evaluation including ROC/AUC	mixed	Read question + answer
Q7a [10]	Unit 4 · Organization and facility mentions	case	Read question + answer · 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

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
Q1b [5]	Unit 1 · Levels of language analysis	theory	Read question + answer
Q1c [5]	Unit 7 · Medical chatbot	theory	Read question + answer
Q1d [5]	Unit 7 · Sentiment with logistic regression	mixed	Read question + answer
Q2a [10]	Unit 5 · CBOW and Skip-gram architecture	theory	Read question + answer
Q2b [10]	Unit 3 · Seven regex constructions	case	Read question + answer
Q3a [10]	Unit 2 · Text preprocessing	theory	Read question + answer
Q3b [10]	Unit 4 · Penn Treebank tagging	case	Read question + answer · 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
Q4b [10]	Unit 7 · Text classification pipeline	theory	Read question + answer
Q5a [10]	Unit 3 · Corpora and structure	theory	Read question + answer
Q5b [10]	Unit 7 · Multinomial Naive Bayes	numerical	Read question + answer
Q6a [10]	Unit 5 · GloVe	theory	Read question + answer
Q6b [10]	Unit 2 · BoW and TF–IDF movie corpus	numerical	Read question + answer
Q7a [10]	Unit 4, 7 · Information extraction, chunking and NER	theory	Read question + answer
Q7b [10]	Unit 5 · Distributional hypothesis and cosine	mixed	Read question + answer

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.