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Explain the various types of ambiguities encountered in Natural Language Processing (NLP). Illustrate each type with suitable examples. ii. Compare top-down and bottom-up parsing and apply bottom-up syntax parsing approach to the sentence “A cat chased the mouse” and draw a parse tree using the grammar rules: S → NP VP; NP → Pronoun | Proper-Noun | Det Nominal; Nominal → Noun | Nominal Noun | Nominal PP; VP → Verb | Verb NP | VP PP; PP → Prep NP. Lexicon: Det → {A, the}; Noun → {cat, mouse}; Verb → {chased}. 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The students listened to the teacher. The board was full of notes. The student asked a question. The teacher answered from the book. Target words: teacher, student, board, book. Context words: wrote, listened, notes, question. i. Construct a co-occurrence matrix for the target and context words. Take the window size as one whole sentence. ii. Evaluate the similarity between the vectors of the target words “teacher-student” and “teacher-board” using the cosine similarity. iii. Plot the target word vectors by considering only two dimensions: “wrote” and “notes”. [10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 5 ], "answerRoute": "/nlp/#tee/papers/final-2025/final-2025-q3b", "answer_route": "/nlp/#tee/papers/final-2025/final-2025-q3b", "sourcePath": "nlp\\sources\\f713f061_QP_Final-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "da887fa371f7ccaf9c226f3253505077890cb0b43d05457e9d805682ebe8512f", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "" }, { "label": "Q4a", "units": [ 2 ], "type": "theory", "sourcePages": [ 2 ], "topic": "Three preprocessing techniques", "id": "nlp-F25-Q4a", "question": "Question. 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Apply Hidden Markov Model (HMM) approach to: 1. Cat/Noun chase/Verb mice/Noun 2. The/Det dog/Noun runs/Verb 3. A/Det cat/Noun eats/Verb cheese/Noun i. Construct the emission probability matrix. ii. Construct the transmission probability matrix. iii. Calculate the probability of sequence of events: The/Det cat/Noun eats/Verb mice/Noun. Note: Include start and end characters for all the sentences. 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[10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 2 ], "answerRoute": "/nlp/#tee/papers/final-2025/final-2025-q5a", "answer_route": "/nlp/#tee/papers/final-2025/final-2025-q5a", "sourcePath": "nlp\\sources\\f713f061_QP_Final-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "5105fb13c4b504af9af489c6d69ff3402af0dd04776de5e0f17b1367eb3dedee", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "" }, { "label": "Q5b", "units": [ 5 ], "type": "numerical", "sourcePages": [ 3 ], "topic": "Naive Bayes word-sense disambiguation", "id": "nlp-F25-Q5b", "question": "Question. Consider the sentence: “The bat hit the ball hard on the cricket field.” Two possible senses of “bat”: Sense 1: Bat (Sports equipment – cricket/baseball); Sense 2: Bat (Nocturnal flying mammal). Sentence ID Context words Sense 1 ball, cricket, score, stadium Sports 2 night, cave, wings, fly Animal 3 player, run, match, bat Sports 4 nocturnal, vampire, insect Animal 5 team, field, bowl, bat Sports 6 dark, flying, cave, night Animal a) Using the Naïve Bayes algorithm, classify the sense of “bat” in the given sentence. Apply Laplace Smoothing wherever applicable. b) Discuss the significance of Laplace Smoothing in Naïve Bayes Classification. 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Both conventions and their consequence are explained." }, { "label": "Q6a", "units": [ 5, 6 ], "type": "theory", "sourcePages": [ 3 ], "topic": "Coreference/pronouns; CBOW and Skip-gram", "id": "nlp-F25-Q6a", "question": "Question. i. Differentiate between co-reference resolution and pronoun resolution with suitable examples. ii. Explain the working principle of the Continuous Bag of Words (CBOW) model and Skipgram model in Word2Vec. Illustrate how it predicts the target word from its surrounding context. [10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 5, 6 ], "answerRoute": "/nlp/#tee/papers/final-2025/final-2025-q6a", "answer_route": "/nlp/#tee/papers/final-2025/final-2025-q6a", "sourcePath": "nlp\\sources\\f713f061_QP_Final-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "3eec89d0df9cae4ffb670d5a40bda0c21dbdeebdbd449990ebceed66c75e049f", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "" }, { "label": "Q6b", "units": [ 6 ], "type": "case", "sourcePages": [ 3 ], "topic": "Hobbs antecedent traversal", "id": "nlp-F25-Q6b", "question": "Question. i. Explain Hobb’s Algorithm. Discuss the steps with help of example and state its limitation. ii. Sentence: Ravi met Suresh at the station after he arrived from Delhi. Using Hobbs’ algorithm, determine the most likely antecedent of the pronoun “he”. Show the steps of traversal according to the algorithm. 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[10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 7 ], "answerRoute": "/nlp/#tee/papers/final-2025/final-2025-q7a", "answer_route": "/nlp/#tee/papers/final-2025/final-2025-q7a", "sourcePath": "nlp\\sources\\f713f061_QP_Final-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "ccab32134e857751f2f9744ee5eb222972d084f10d28fe14d983fc36ab9831a0", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "" }, { "label": "Q7b", "units": [ 2, 3 ], "type": "mixed", "sourcePages": [ 4 ], "topic": "Corpus structures and Jaccard example", "id": "nlp-F25-Q7b", "question": "Question. i. Differentiate between a flat text corpus and an annotated corpus, and discuss how each can be applied in various NLP tasks with suitable examples. ii. Explain a real-world scenario where the Jaccard Similarity measure is more appropriate than Euclidean or Manhattan distance, and justify why Jaccard is the best choice in that case. 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Draw block diagram of generic NLP pipeline and explain the task associated with each block. 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Which category (function words and content words) belong to open class and closed class? Justify your answer. iii. For the sentence “The small cat is sleeping under the tree”, identify the function words and content words. 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Which type of corpus would be more suitable for each method, and why? [5 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 5, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 5 ], "answerRoute": "/nlp/#tee/papers/reexam-2026/reexam-2026-q1c", "answer_route": "/nlp/#tee/papers/reexam-2026/reexam-2026-q1c", "sourcePath": "nlp\\sources\\6f12e9c9_QP_Re-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "ecd988cd26ccfb0aac40ffa074791937b7e4315bcf7cf515ba0d048ef7e1c4c9", "status": "complete", "scope": "current syllabus", "retainedMarks": 5, "excludedTask": null, "internalSubparts": [], "notes": "" }, { "label": "Q1d", "units": [ 6 ], "type": "theory", "sourcePages": [ 1 ], "topic": "Cataphora, anaphora and coreference", "id": "nlp-R26-Q1d", "question": "Question. Differentiate between cataphora and anaphora in terms of direction of reference. How coreference resolution is different from anaphora reference? [5 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 5, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 6 ], "answerRoute": "/nlp/#tee/papers/reexam-2026/reexam-2026-q1d", "answer_route": "/nlp/#tee/papers/reexam-2026/reexam-2026-q1d", "sourcePath": "nlp\\sources\\6f12e9c9_QP_Re-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "253eb157dbe50f7d173164c99bae1c5bf550c5c8641f5ea1917615d0071560f9", "status": "complete", "scope": "current syllabus", "retainedMarks": 5, "excludedTask": null, "internalSubparts": [], "notes": "" }, { "label": "Q2a", "units": [ 1 ], "type": "theory", "sourcePages": [ 1 ], "topic": "Five NLP tasks; syntax and semantics", "id": "nlp-R26-Q2a", "question": "Question. i. Discuss five different tasks associated with Natural Language Processing. ii. Compare and contrast syntactic analysis and semantic analysis in NLP. Support your explanation with examples. [10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 1 ], "answerRoute": "/nlp/#tee/papers/reexam-2026/reexam-2026-q2a", "answer_route": "/nlp/#tee/papers/reexam-2026/reexam-2026-q2a", "sourcePath": "nlp\\sources\\6f12e9c9_QP_Re-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "2ef9e89fdf2775c93a1dca02d9ca3ea46985bc36e19c0ea9966ab36cb1eee230", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "" }, { "label": "Q2b", "units": [ 3 ], "type": "numerical", "sourcePages": [ 1 ], "topic": "Add-one bigram sentence probability", "id": "nlp-R26-Q2b", "question": "Question. Consider the following training corpus: 1. cats chase mice in the garden 2. dogs bark loudly at night 3. birds sing in the morning 4. children play games in the park Using a bigram language model with Laplace smoothing, evaluate the probability of the test sentence: “cats bark in the park.” Assume and as start and end of the sentence. [10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 3 ], "answerRoute": "/nlp/#tee/papers/reexam-2026/reexam-2026-q2b", "answer_route": "/nlp/#tee/papers/reexam-2026/reexam-2026-q2b", "sourcePath": "nlp\\sources\\6f12e9c9_QP_Re-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "b771b2c8ed43eebdf850eaff30d8c8e3340d26be6a95d34d995b18686ca17f6c", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "" }, { "label": "Q3a", "units": [ 4 ], "type": "case", "sourcePages": [ 1, 2 ], "topic": "Top-down CFG parsing", "id": "nlp-R26-Q3a", "question": "Question. In the context of natural language processing, explain the concept of top-down and bottom-up parsing approaches. Apply top-down syntax parsing approach to the sentence “The tall boy saw a girl in the park.” and draw a parse tree using the CFG grammar rules: S → NP VP; NP → Det Adj N | Det N; VP → V NP | VP PP; PP → P NP. Lexicon: Det → {the | a}; Adj → {tall}; N → {boy | girl | park}; V → {saw}; P → {in}. [10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 4 ], "answerRoute": "/nlp/#tee/papers/reexam-2026/reexam-2026-q3a", "answer_route": "/nlp/#tee/papers/reexam-2026/reexam-2026-q3a", "sourcePath": "nlp\\sources\\6f12e9c9_QP_Re-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "b35b70ff6b23b7c80be5eb57bac68f375f13c85fc438ea4b1fa490e19955cac6", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "" }, { "label": "Q3b", "units": [ 5 ], "type": "numerical", "sourcePages": [ 2 ], "topic": "Lesk with supplied glosses", "id": "nlp-R26-Q3b", "question": "Question. Consider the sentence: “The bank can ensure your deposits are safe.” The word “bank” has the following two possible senses (WordNet-style glosses): Bank₁: Sloping land especially beside a body of water. Bank₂: A financial institution that accepts deposits and channels the money into lending activities. i. Apply the LESK algorithm to determine the correct sense of the word “bank” in the given sentence. Show the steps clearly. ii. Briefly discuss limitations of the LESK algorithm in handling ambiguous words in real-world contexts. [10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 5 ], "answerRoute": "/nlp/#tee/papers/reexam-2026/reexam-2026-q3b", "answer_route": "/nlp/#tee/papers/reexam-2026/reexam-2026-q3b", "sourcePath": "nlp\\sources\\6f12e9c9_QP_Re-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "4ecde9687cf3a41014be39d5ef3d8a8f761587086a5135bdad6ece07da09f72a", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "" }, { "label": "Q4a", "units": [ 2 ], "type": "numerical", "sourcePages": [ 2 ], "topic": "Bag of words and three cosine similarities", "id": "nlp-R26-Q4a", "question": "Question. Consider the following three documents: Document A: “The dog chased the cat.” Document B: “The cat chased the mouse.” Document C: “The dog ran after the cat.” Using the Bag of Words (BoW) model, represent each document as a vector after removing the stop words {The, by, was}. i. Construct the term-document matrix. ii. Compute the cosine similarity between each pair of documents (A–B, A–C, and B–C). iii. Interpret and comment on the results obtained in terms of document similarity. [10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 2 ], "answerRoute": "/nlp/#tee/papers/reexam-2026/reexam-2026-q4a", "answer_route": "/nlp/#tee/papers/reexam-2026/reexam-2026-q4a", "sourcePath": "nlp\\sources\\6f12e9c9_QP_Re-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "938f2ae44da45f8be23c49d7923867464e6d58c9f6f2796ba9a73dd83dc0d742", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "" }, { "label": "Q4b", "units": [ 4 ], "type": "numerical", "sourcePages": [ 2 ], "topic": "HMM estimation and given tag-path joint probability", "id": "nlp-R26-Q4b", "question": "Question. Consider the following POS tagged corpus. Apply Hidden Markov Model (HMM) approach to: 1. Dog/Noun eats/Verb bread/Noun 2. The/Det cat/Noun sleeps/Verb 3. A/Det dog/Noun eats/Verb fish/Noun i. Construct the emission probability matrix. ii. Construct the transmission probability matrix. iii. Calculate the probability of the given sequence of events: The/Det dog/Noun eats/Verb bread/Noun. Note: Include start and end characters for all the sentences. [10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 4 ], "answerRoute": "/nlp/#tee/papers/reexam-2026/reexam-2026-q4b", "answer_route": "/nlp/#tee/papers/reexam-2026/reexam-2026-q4b", "sourcePath": "nlp\\sources\\6f12e9c9_QP_Re-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "a019d3925ee02cb67aafd5a03898745233a57a6a7276b0a908c96534512075e5", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "" }, { "label": "Q5a", "units": [ 2 ], "type": "mixed", "sourcePages": [ 2 ], "topic": "Explain and illustrate TF-IDF", "id": "nlp-R26-Q5a", "question": "Question. Justify with an appropriate example the importance of TF-IDF technique in the document representation and information retrieval task. [10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 2 ], "answerRoute": "/nlp/#tee/papers/reexam-2026/reexam-2026-q5a", "answer_route": "/nlp/#tee/papers/reexam-2026/reexam-2026-q5a", "sourcePath": "nlp\\sources\\6f12e9c9_QP_Re-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "f3ae70a8c7a13d24c1e934401098a4c3b843b42ef5ca355cb5b1ead1b606c8b3", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "" }, { "label": "Q5b", "units": [ 5 ], "type": "numerical", "sourcePages": [ 2, 3 ], "topic": "Co-occurrence, cosine and normalized plot", "id": "nlp-R26-Q5b", "question": "Question. Consider the paragraph: The farmer planted seeds in the soil. The rain watered the soil. The sun helped the plants grow. The farmer harvested the plants. The soil became rich and fertile. Target words: farmer, soil, sun, plant. Context words: plant, water, grow, harvest. i. Construct a co-occurrence matrix for the target and context words. Take the window size as one whole sentence. ii. Evaluate the similarity between the vectors of the target words “farmer-soil”, “farmer-sun”, and “farmer-plant” using the cosine similarity. iii. Plot the target word vectors by considering only two dimensions: “planted” and “harvested”. [10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 5 ], "answerRoute": "/nlp/#tee/papers/reexam-2026/reexam-2026-q5b", "answer_route": "/nlp/#tee/papers/reexam-2026/reexam-2026-q5b", "sourcePath": "nlp\\sources\\6f12e9c9_QP_Re-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "afb76ff76cc18fb8516b81e4bc372a2acd8b576dce82983444cdcc29043cb5a7", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "Target/context normalization and sentence-presence convention stated; alternative target lemmatization has a separately calculated result." }, { "label": "Q6a", "units": [ 5, 6 ], "type": "theory", "sourcePages": [ 3 ], "topic": "Pronoun-resolution methods; homonymy and synonymy", "id": "nlp-R26-Q6a", "question": "Question. i. Briefly describe any two approaches to pronoun resolution and mention one advantage of each. ii. Describe the difference between Homonymy and Synonymy with suitable examples. Discuss how these two relations differently affect natural language processing applications? 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State its main steps and limitations. ii. Apply Hobbs’ Algorithm to the sentence: “When Ramesh gave Shyam a book, he smiled.” Identify the antecedent of “he” by showing traversal steps. [10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 6 ], "answerRoute": "/nlp/#tee/papers/reexam-2026/reexam-2026-q6b", "answer_route": "/nlp/#tee/papers/reexam-2026/reexam-2026-q6b", "sourcePath": "nlp\\sources\\6f12e9c9_QP_Re-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "fd938f911e4fc8f4d577f645cf402e937c6ecc57de4cf0e17cd313f029c612f8", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "Hobbs requires a parse and compatibility assumptions. The assumed parse and traversal order are disclosed." }, { "label": "Q7a", "units": [ 7 ], "type": "theory", "sourcePages": [ 3 ], "topic": "Information-retrieval stages", "id": "nlp-R26-Q7a", "question": "Question. i. Explain the concept of Information Retrieval, highlighting its role in Recommender Systems and Search Engines. ii. Discuss the various steps involved in the Information Retrieval process. [10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 7 ], "answerRoute": "/nlp/#tee/papers/reexam-2026/reexam-2026-q7a", "answer_route": "/nlp/#tee/papers/reexam-2026/reexam-2026-q7a", "sourcePath": "nlp\\sources\\6f12e9c9_QP_Re-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "fbe50608f9599d513b01a9852002f5ffc7171ff6f7ea3f1be46bb8a0d3f57ce5", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "" }, { "label": "Q7b", "units": [ 2, 3 ], "type": "theory", "sourcePages": [ 3 ], "topic": "Gutenberg and text normalization", "id": "nlp-R26-Q7b", "question": "Question. i. Discuss how the Gutenberg Corpus can be utilized for various text analysis tasks in Natural Language Processing? ii. Explain the importance of text normalization in Natural Language Processing (NLP). Illustrate your answer with a suitable example of an application where text normalization plays a crucial role. [10 marks]", "promptStatus": "Student-facing normalized transcription, including stated assumptions; compare source for original typography.", "marks": 10, "marksStatus": "Printed whole lettered-part allocation; original marks retained even where a legacy extract is excluded.", "unit": [ 2, 3 ], "answerRoute": "/nlp/#tee/papers/reexam-2026/reexam-2026-q7b", "answer_route": "/nlp/#tee/papers/reexam-2026/reexam-2026-q7b", "sourcePath": "nlp\\sources\\6f12e9c9_QP_Re-Exam_Natural_Language_Processing_702AI0C009__Semester_V_2025-2026.pdf", "answerHtmlSha256": "c1bd7a4f8565d27cd9a63fe94f57a366260a191838375ebe15a6bcc1f7fd6934", "status": "complete", "scope": "current syllabus", "retainedMarks": 10, "excludedTask": null, "internalSubparts": [], "notes": "" } ] }, { "id": "nlp-F23", "subject": "nlp", "code": "F23", "date": "2023-12-01", "type": "final", "sourceFile": "Natural Language Processing ,Final exam 2023-24.pdf", "sourceSha256": "ebd3d53c7c427de54713dfc7d59ae84a1a1d25a91280658464c1ada7b1d7e97a", "sourcePages": 3, "visualReview": { "status": "fresh: every original page visually inspected 2026-10-14", "receipt": "audit/dl_nlp_all_pyq_review_20261014.md" }, "pattern": { "durationMinutes": 180, "printedMarks": 120, "attemptMarks": 100, "compulsory": [ "Q1" ], "choose": 4, "from": [ "Q2", "Q3", "Q4", "Q5", "Q6" ], "note": "Original has six main questions on three pages: Q1 compulsory and any four of Q2–Q6. Offered total 120, attempted 100." }, "questions": [ { "id": "nlp-F23-Q1a", "label": "Q1a", "question": "Define Word Sense Disambiguation and Pronoun Resolution. Give an example of each.", "marks": 5, "marksStatus": "Printed allocation", "unit": [ 5, 6 ], "units": [ 5, 6 ], "topic": "WSD and pronoun resolution", "type": "theory", "sourcePages": [ 1 ], "answerRoute": "/nlp/#tee/papers/legacy-f23/q1a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "eaccdcca2a99b15586b1cd84ae88b737b0412c6d973d1369243638bbecec4717" }, { "id": "nlp-F23-Q1b", "label": "Q1b", "question": "Define WordNet. Describe the WordNet hierarchy.", "marks": 5, "marksStatus": "Printed allocation", "unit": [ 5 ], "units": [ 5 ], "topic": "WordNet and its hierarchy", "type": "theory", "sourcePages": [ 1 ], "answerRoute": "/nlp/#tee/papers/legacy-f23/q1b", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "1059dd7ae47006279861196e48237dd61fa4c2041ef2db094b954146e3275b2e" }, { "id": "nlp-F23-Q1c", "label": "Q1c", "question": "Extract x₁ = log₁₀(word count), x₂ = count of positive lexicon words and x₃ = count of negative lexicon words from: “I was amazed at how the product quickly it arrived. The customer service was exceptional. The support team responded promptly and professionally. I was not sad with my decision. 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Numbers in the explanation are authored illustrations, not original numerical givens.", "internalSubparts": [], "answerHtmlSha256": "0840b5537bbb3a6263c9061c01a73d7e1887b25282737c6ddae19e26f43d4d46" }, { "id": "nlp-R24-Q6b", "label": "Q6b", "question": "For the four tagged sentences below construct transition and emission tables (3 marks each), then calculate Geeta/N will/M spot/V Rita/N using initial P(N)=0.5, P(M)=0.25, P(V)=0.25.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 4 ], "units": [ 4 ], "topic": "HMM tables and path", "type": "numerical", "sourcePages": [ 3 ], "answerRoute": "/nlp/#tee/papers/legacy-r24/q6b", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [ { "label": "i", "marks": 3, "topic": "Transitions" }, { "label": "ii", "marks": 3, "topic": "Emissions" }, { "label": "iii", "marks": null, "topic": "Path: remaining four inferred, not printed on this copy" } ], "answerHtmlSha256": "42f09e5a9fa93dd3fabdd4ff2f38448a387c881f860659b9139dbd89d2184faa", "answerTaskChecklist": [ { "task": "Transition matrix", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Emission matrix", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Given-path probability", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-R24-Q7a", "label": "Q7a", "question": "Explain NLP use cases in healthcare and finance.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 7 ], "units": [ 7 ], "topic": "Healthcare and finance applications", "type": "theory", "sourcePages": [ 4 ], "answerRoute": "/nlp/#tee/papers/legacy-r24/q7a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "596119ccb6debb3130d6adbb06f1fb06301ba99bb989043b11408859e41cb368" }, { "id": "nlp-R24-Q7b", "label": "Q7b", "question": "Train Naive Bayes on: positive “amaze wonderful great”; “happy happy great”; “amaze great happy disgust”; “great happy wonderful”. Negative “disgust sad terrible great”; “sad terrible sad happy”. Predict sentence 7: “great happy disgust great”.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 7 ], "units": [ 7 ], "topic": "Naive Bayes sentiment", "type": "numerical", "sourcePages": [ 4 ], "answerRoute": "/nlp/#tee/papers/legacy-r24/q7b", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "3cf85f7827fd4970b14f7c8cd906230e3f1e64eef67e58792c287d654ae58edd" } ] }, { "id": "nlp-F22", "subject": "nlp", "code": "F22", "date": "2022-11-22", "type": "final", "sourceFile": "Natural Language Processing, 3rd Year, Sem-V, Year 2022-23, Final Exam.pdf", "sourceSha256": "bf0fe88ad06076fcbf8f3ff6814575270da0ee7ffb03a5633eae66d56871440c", "sourcePages": 4, "visualReview": { "status": "fresh: every original page visually inspected 2026-10-14", "receipt": "audit/dl_nlp_all_pyq_review_20261014.md" }, "pattern": { "durationMinutes": 180, "printedMarks": 140, "attemptMarks": 100, "compulsory": [ "Q1" ], "choose": 4, "from": [ "Q2", "Q3", "Q4", "Q5", "Q6", "Q7" ], "note": "" }, "questions": [ { "id": "nlp-F22-Q1a", "label": "Q1a", "question": "Explain the need for NLP and the difficulties in natural language processing.", "marks": 5, "marksStatus": "Printed allocation", "unit": [ 1 ], "units": [ 1 ], "topic": "Need and difficulties of NLP", "type": "theory", "sourcePages": [ 1 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q1a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "c6bb1976f787d8d4158d87ce0c5be9d54b8e58cbc0ceea39b8fa52e37fa50863" }, { "id": "nlp-F22-Q1b", "label": "Q1b", "question": "Explain the levels of language analysis with examples.", "marks": 5, "marksStatus": "Printed allocation", "unit": [ 1 ], "units": [ 1 ], "topic": "Levels of language analysis", "type": "theory", "sourcePages": [ 1 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q1b", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "1e17a6d1e84194ebe3cccfe87bd845091ee5f22801c8d07605d7813f231d0883" }, { "id": "nlp-F22-Q1c", "label": "Q1c", "question": "Briefly explain a medical chatbot and its NLP processing.", "marks": 5, "marksStatus": "Printed allocation", "unit": [ 7 ], "units": [ 7 ], "topic": "Medical chatbot", "type": "theory", "sourcePages": [ 1 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q1c", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "e7eec72e39d3e8e772af561a5c2de9435c786da3ff5d4f38f40c34f594740b2d" }, { "id": "nlp-F22-Q1d", "label": "Q1d", "question": "Explain sentiment analysis using logistic regression with an example.", "marks": 5, "marksStatus": "Printed allocation", "unit": [ 7 ], "units": [ 7 ], "topic": "Sentiment with logistic regression", "type": "mixed", "sourcePages": [ 1 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q1d", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "adc3033d894f1a912a1d4e9dcd7a7b0ee68a0928edef4f0ead814369721f8bdf" }, { "id": "nlp-F22-Q2a", "label": "Q2a", "question": "Explain CBOW and Skip-gram with suitable architecture diagrams.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 5 ], "units": [ 5 ], "topic": "CBOW and Skip-gram architecture", "type": "theory", "sourcePages": [ 1 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q2a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "8106bf83370d80f41a7518b56833c0aba3ea417b5a078a91b4501f7c6df23db3" }, { "id": "nlp-F22-Q2b", "label": "Q2b", "question": "Define regular expressions and their uses. Write expressions for (i) all alphabetic strings, (ii) lowercase alphabetic strings ending in b, (iii) a/b strings where each a is immediately preceded and followed by b, (iv) consecutive repeated words, (v) a line starting with an integer and ending with a word, (vi) strings containing the whole words grotto and raven, (vii) capture the first English word, handling opening punctuation.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 3 ], "units": [ 3 ], "topic": "Seven regex constructions", "type": "case", "sourcePages": [ 1, 2 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q2b", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "82d6149266d5f3d9e5d435955b5eb1a32fadbf41508863ccc00a1f2466c1f8fc", "answerTaskChecklist": [ { "task": "Regex definition and uses", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Alphabetic strings", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Lowercase ending b", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "a surrounded by b", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Repeated adjacent words", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Integer-start word-end", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Whole grotto and raven", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "First-word capture", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-F22-Q3a", "label": "Q3a", "question": "Explain text preprocessing with applications and examples.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 2 ], "units": [ 2 ], "topic": "Text preprocessing", "type": "theory", "sourcePages": [ 2 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q3a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "44ca2b835ed3ee248a23f56739b44c84aa20c68d8f754879d4de7ce46f03d121" }, { "id": "nlp-F22-Q3b", "label": "Q3b", "question": "Using Penn Treebank tags, tag every word and ignore punctuation: (1) It is a nice night. (2) This crap game is over a garage in Fifty-second Street. (3) Nobody ever takes the newspapers she sells. (4) I am sitting in Mindy’s restaurant putting on the gefilte fish, which is a dish I am very fond of. (5) What flights do you have from Milwaukee to Tampa?", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 4 ], "units": [ 4 ], "topic": "Penn Treebank tagging", "type": "case", "sourcePages": [ 2 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q3b", "status": "complete with explicit source ambiguity", "scope": "current syllabus", "notes": "Context-sensitive tags and possessive tokenization are explicitly stated; reasonable corpus guidelines can differ on noun adjuncts.", "internalSubparts": [], "answerHtmlSha256": "9c5003124fef2dc9f65f535a4e78befc921307aaaa93f7f21647074bee70a413", "answerTaskChecklist": [ { "task": "Sentence1 complete tags", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Sentence2 complete tags", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Sentence3 complete tags", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Sentence4 complete tags", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Sentence5 complete tags", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-F22-Q4a", "label": "Q4a", "question": "Explain the mention-pair model with a detailed example.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 6 ], "units": [ 6 ], "topic": "Mention-pair coreference", "type": "theory", "sourcePages": [ 2 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q4a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "d4b8b698a5b88633477a05ea11d9dddf5058200923f97baad227b25fe8d0f61a" }, { "id": "nlp-F22-Q4b", "label": "Q4b", "question": "Explain the text-classification pipeline with a diagram.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 7 ], "units": [ 7 ], "topic": "Text classification pipeline", "type": "theory", "sourcePages": [ 2 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q4b", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "16ac8d8e0a52f95057ea79979f181b1bca9bd025db5ab37128c1f2f711ab115f" }, { "id": "nlp-F22-Q5a", "label": "Q5a", "question": "Define a corpus. Explain corpus types, characteristics and structure.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 3 ], "units": [ 3 ], "topic": "Corpora and structure", "type": "theory", "sourcePages": [ 2 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q5a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "3a63cdab87dca446bd2bbbb232a1ce93a00c6565684791ff85bf2b788f46aef3" }, { "id": "nlp-F22-Q5b", "label": "Q5b", "question": "Explain Naive Bayes for text classification. Using the four documents below, classify “Chinese Chinese Chinese Tokyo Japan”: Chinese Beijing Chinese=yes; Chinese Chinese Shanghai=yes; Chinese Macao=yes; Tokyo Japan Chinese=no.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 7 ], "units": [ 7 ], "topic": "Multinomial Naive Bayes", "type": "numerical", "sourcePages": [ 2, 3 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q5b", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "84151c03e00f0cc1418bd6efc22304d3de1c66afa4151c1d2b3a41222138fc37", "answerTaskChecklist": [ { "task": "Naive Bayes theory", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Training vocabulary and counts", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Class priors", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Repeated-word test likelihood", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Classification", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-F22-Q6a", "label": "Q6a", "question": "Explain GloVe word embeddings.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 5 ], "units": [ 5 ], "topic": "GloVe", "type": "theory", "sourcePages": [ 3 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q6a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "cad12948d46d4702cedd39c6d3727d8a4f069b679a65e4eeb870c4c92a3a7741" }, { "id": "nlp-F22-Q6b", "label": "Q6b", "question": "Find Bag of Words and TF–IDF for: “This movie is very scary and long”; “This movie is not scary and is slow”; “This movie is spooky and good”.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 2 ], "units": [ 2 ], "topic": "BoW and TF–IDF movie corpus", "type": "numerical", "sourcePages": [ 3 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q6b", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "79877fa19124ab8367cbb7a122a776008d4ed57b84420a514d0691184c57064a", "answerTaskChecklist": [ { "task": "Vocabulary", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "BoW", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Document frequencies", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "IDF", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Complete TF-IDF matrix", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-F22-Q7a", "label": "Q7a", "question": "Explain information-extraction architecture with a diagram. Explain chunking and named-entity recognition with examples.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 4, 7 ], "units": [ 4, 7 ], "topic": "Information extraction, chunking and NER", "type": "theory", "sourcePages": [ 3 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q7a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "0c37ba33af65b150ba007107e75a9bff3f5bbe3ef4d89128d2e0d2eeac89f14d", "answerTaskChecklist": [ { "task": "Information-extraction architecture", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Chunking example", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "NER example", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-F22-Q7b", "label": "Q7b", "question": "Justify “You shall know a word by the company it keeps.” Calculate cosine similarity of w₁=[0.2,0.1,0.3,0.4] and w₂=[0.3,0,0.2,0.5].", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 5 ], "units": [ 5 ], "topic": "Distributional hypothesis and cosine", "type": "mixed", "sourcePages": [ 3 ], "answerRoute": "/nlp/#tee/papers/legacy-f22/q7b", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "03ac6fe747459af1459f0086aa0b6e65ccab8ae4e8e0c37d972261096e8add9d", "answerTaskChecklist": [ { "task": "Distributional hypothesis justification", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Dot product", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Norms", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Cosine", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] } ] }, { "id": "nlp-R23", "subject": "nlp", "code": "R23", "date": "2023-01-30", "type": "re-exam", "sourceFile": "Natural Language Processing, 3rd Year, Sem-V, Year 2022-23, Re-Exam.pdf", "sourceSha256": "f9a8b3480cd7e7c2884d13f5513b2f7f6e88aca65314d1873bb95966de28e15b", "sourcePages": 5, "visualReview": { "status": "fresh: every original page visually inspected 2026-10-14", "receipt": "audit/dl_nlp_all_pyq_review_20261014.md" }, "pattern": { "durationMinutes": 180, "printedMarks": 140, "attemptMarks": 100, "compulsory": [ "Q1" ], "choose": 4, "from": [ "Q2", "Q3", "Q4", "Q5", "Q6", "Q7" ], "note": "Q1 header is unclear/conflicting [14/20], but all four parts print 5 marks each; sum=20. Main paper is 100 attempted marks." }, "questions": [ { "id": "nlp-R23-Q1a", "label": "Q1a", "question": "“The vocabulary of a corpus remains the same before and after normalization.” State true or false and justify with an example.", "marks": 5, "marksStatus": "Printed allocation", "unit": [ 2 ], "units": [ 2 ], "topic": "Normalization changes vocabulary", "type": "theory", "sourcePages": [ 1 ], "answerRoute": "/nlp/#tee/papers/legacy-r23/q1a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "a507877b8a511d8edda44ff4a002f9589268518cc993991044c14fe5476f6721" }, { "id": "nlp-R23-Q1b", "label": "Q1b", "question": "State the characteristics of a good corpus.", "marks": 5, "marksStatus": "Printed allocation", "unit": [ 3 ], "units": [ 3 ], "topic": "Good corpus characteristics", "type": "theory", "sourcePages": [ 1 ], "answerRoute": "/nlp/#tee/papers/legacy-r23/q1b", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "da1f826da81b0ae5b39456857c944281bff0de7bcc6695f029054cadba89d57e" }, { "id": "nlp-R23-Q1c", "label": "Q1c", "question": "Differentiate Word2Vec and Doc2Vec.", "marks": 5, "marksStatus": "Printed allocation", "unit": [ 5 ], "units": [ 5 ], "topic": "Word2Vec versus Doc2Vec", "type": "theory", "sourcePages": [ 1 ], "answerRoute": "/nlp/#tee/papers/legacy-r23/q1c", "status": "complete", "scope": "legacy named-method extension; not separately named in current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "d321a25209b445278b27b55e425e27e4dd4e985b1a2dedf1ea6807e3c574e974" }, { "id": "nlp-R23-Q1d", "label": "Q1d", "question": "Explain the types of ambiguity in NLP with examples.", "marks": 5, "marksStatus": "Printed allocation", "unit": [ 1, 4 ], "units": [ 1, 4 ], "topic": "Ambiguity in NLP", "type": "theory", "sourcePages": [ 1 ], "answerRoute": "/nlp/#tee/papers/legacy-r23/q1d", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "00fe07f5559aeec2bd98432afed067220fdb1cbc7349946204cdc99ab905660a" }, { "id": "nlp-R23-Q2a", "label": "Q2a", "question": "Perform sentence segmentation, word tokenization, stopword removal, lowercasing and stemming on the following paragraph; show vocabulary before and after: “Tanisha and Simran are best friends. They play together with other friends. Tanisha likes to play badminton but Simran prefers to play online games. Tanisha wants to be a badminton player. 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source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Precision", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Recall", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "F1", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "ROC", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "AUC", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-R23-Q7a", "label": "Q7a", "question": "Identify organization and facility mentions in this White House visitor passage using the supplied ACE categories: “After the 9/11 attacks, the system changed radically. 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At the head of the line, rangers from the National Park Service check photo IDs against a list of names.”", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 4 ], "units": [ 4 ], "topic": "Organization and facility mentions", "type": "case", "sourcePages": [ 3, 4 ], "answerRoute": "/nlp/#tee/papers/legacy-r23/q7a", "status": "complete with explicit source ambiguity", "scope": "current syllabus", "notes": "Boundary and generic-mention decisions depend on the provided ACE annotation convention; the answer distinguishes them from a named-only interpretation.", "internalSubparts": [], "answerHtmlSha256": "6daf6acaf8e80fb481884301a2f88e557a1b5f889ee27ea7e4e646a4a95c31b3", "answerTaskChecklist": [ { "task": "All organization/facility mentions", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "White House context distinction", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Named versus generic convention", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-R23-Q7b", "label": "Q7b", "question": "Explain POS tagging. 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List real-world NLP applications that use these concepts.", "marks": null, "marksStatus": "Not printed beside Q1 part; do not invent 5 marks", "unit": [ 1, 7 ], "units": [ 1, 7 ], "topic": "NLU versus NLG and applications", "type": "theory", "sourcePages": [ 1 ], "answerRoute": "/nlp/#tee/papers/legacy-r25/q1d", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "2f924a5e526bf89877db8971a3dd69b84297e7da5f2dcf81df3a14b0ee1143bd", "answerTaskChecklist": [ { "task": "NLU/NLG comparison", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Applications", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-R25-Q2a", "label": "Q2a", "question": "For “The cat sat on the mat.”, “The dog barked at the cat.” and “I love my dog and my cat.” remove stopwords {I,the,on,my,at,and}; construct TF–IDF and calculate cosine similarity between the first two documents.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 2 ], "units": [ 2 ], "topic": "TF–IDF cosine after a fixed stoplist", "type": "numerical", "sourcePages": [ 1, 2 ], "answerRoute": "/nlp/#tee/papers/legacy-r25/q2a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "670bb14febbfc479ea370c1f3de604d662e4f1020b638473a5828b0f75244085", "answerTaskChecklist": [ { "task": "Exact-stoplist vocabulary", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "TF-IDF matrix", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Prominent terms", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Cosine first two documents", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-R25-Q2b", "label": "Q2b", "question": "Use the supplied class/conditional probabilities to predict Play Tennis for Outlook=Sunny, Temperature=Cool, Humidity=High, Windy=True.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 7 ], "units": [ 7 ], "topic": "Weather Naive Bayes", "type": "numerical", "sourcePages": [ 2 ], "answerRoute": "/nlp/#tee/papers/legacy-r25/q2b", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "e0054c2e41e3163d8b1614a1c5354a69a5ea7d974834d9ac077388f145dbffb4", "answerTaskChecklist": [ { "task": "Given priors/conditionals", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Yes product", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "No product", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Comparison", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-R25-Q3a", "label": "Q3a", "question": "Explain coreference and anaphoric reference with examples.", "marks": 5, "marksStatus": "Printed allocation", "unit": [ 6 ], "units": [ 6 ], "topic": "Coreference and anaphora", "type": "theory", "sourcePages": [ 2 ], "answerRoute": "/nlp/#tee/papers/legacy-r25/q3a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "ae7e476fbed96e2b84ec01a0f33b570722528ec384196d541fcc280689c06201", "answerTaskChecklist": [ { "task": "Reference-resolution need", "status": "addressed; 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source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Coherence", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Discourse markers", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Examples", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-R25-Q3c", "label": "Q3c", "question": "Explain a question-answering system with its architecture and an example.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 7 ], "units": [ 7 ], "topic": "Question answering system", "type": "theory", "sourcePages": [ 2 ], "answerRoute": "/nlp/#tee/papers/legacy-r25/q3c", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "a5bc79a9f9f5d9b24946ba2247433ca5187d67c689582f315b474aeddb1762f5", "answerTaskChecklist": [ { "task": "QA blocks and diagram", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Real-world use", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-R25-Q4a", "label": "Q4a", "question": "Explain the stages of an NLP pipeline.", "marks": 5, "marksStatus": "Printed allocation", "unit": [ 1 ], "units": [ 1 ], "topic": "NLP pipeline", "type": "theory", "sourcePages": [ 2 ], "answerRoute": "/nlp/#tee/papers/legacy-r25/q4a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "0fa25e15735fc53709ee985d07f33b3cdb4839c54e7c2dc74f07b3e4f2f6ccbc", "answerTaskChecklist": [ { "task": "NLP definition", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Pipeline diagram", "status": "addressed; 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Other printed lettered allocations determine each 20-mark main question." }, "questions": [ { "id": "nlp-F24-Q1a", "label": "Q1a", "question": "Write TF–IDF expressions and explain their role in information retrieval.", "marks": null, "marksStatus": "Not printed beside Q1 part; do not invent 5 marks", "unit": [ 2, 7 ], "units": [ 2, 7 ], "topic": "TF–IDF in retrieval", "type": "theory", "sourcePages": [ 1 ], "answerRoute": "/nlp/#tee/papers/legacy-f24/q1a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "e358601522fc4abacf7aa86b87198238b66020d77a16cfec80c3ff2dfcb2fa9b" }, { "id": "nlp-F24-Q1b", "label": "Q1b", "question": "Explain NER with examples and approaches.", "marks": null, "marksStatus": "Not printed beside Q1 part; do not invent 5 marks", "unit": [ 4 ], "units": [ 4 ], "topic": "NER and approaches", "type": "theory", "sourcePages": [ 1 ], "answerRoute": "/nlp/#tee/papers/legacy-f24/q1b", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "470e7683da3c87521737715448da9e8044635f49d344f1b5c5ae5e0f30b4b5ee" }, { "id": "nlp-F24-Q1c", "label": "Q1c", "question": "Explain Skip-gram and CBOW.", "marks": null, "marksStatus": "Not printed beside Q1 part; 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Parse “the cat chased the dog” using S→NP VP, NP→Det N, VP→V NP, Det→the, N→cat|dog, V→chased|saw.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 4 ], "units": [ 4 ], "topic": "Top-down syntax tree", "type": "case", "sourcePages": [ 3 ], "answerRoute": "/nlp/#tee/papers/legacy-f24/q4a", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "ce865db983903af38551de012bc48a94504641f487b464f0abee4ccd8d69991e", "answerTaskChecklist": [ { "task": "Parsing need", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Top-down versus bottom-up", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Supplied-rule derivation", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Parse tree", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-F24-Q4b", "label": "Q4b", "question": "Explain NLP data acquisition, why augmentation is needed, and any four augmentation methods.", "marks": 10, "marksStatus": "Printed allocation", "unit": [ 3 ], "units": [ 3 ], "topic": "Data acquisition and four augmentation methods", "type": "theory", "sourcePages": [ 3 ], "answerRoute": "/nlp/#tee/papers/legacy-f24/q4b", "status": "complete", "scope": "current syllabus", "notes": "", "internalSubparts": [], "answerHtmlSha256": "c85837cd9a02c1329ecbe8a54810d50fd72c4bd38e049f9c5a6cf6eda1857fb7", "answerTaskChecklist": [ { "task": "Data acquisition approaches", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Augmentation need", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" }, { "task": "Four augmentation methods", "status": "addressed; source ambiguity stated where applicable", "marks": null, "marksRule": "shares parent allocation; do not sum separately" } ] }, { "id": "nlp-F24-Q5a", "label": "Q5a", "question": "Explain text feature engineering and list vectorization approaches. 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