B.TechSemester 72023-24Text Analytics And Natural Langugae ProcessingKAI073
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Text Analytics And Natural Langugae Processing (KAI073) - AKTU Question Paper 2023-24

B.Tech · Semester 7 · Free PDF Download

This is the official AKTU Text Analytics And Natural Langugae Processing Previous Year Question Paper for B.Tech Semester 7, academic session 2023-24. Published by Dr. A.P.J. Abdul Kalam Technical University (AKTU/UPTU), Lucknow. Free PDF download — no login required.

Course:B.Tech
Semester:Semester 7
Session:2023-24
University:AKTU / UPTU

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Questions Asked in 2023-24

Text Analytics And Natural Langugae Processing (KAI073) — complete question paper · 100 marks · 3 Hours

Section AAttempt all q u e s t i o n s i n b r i e f . 2 x 10 = 20
  • a
    What are the key challenges in processing human language? 2
  • b
    How do these linguistic principles form the foundation of NLP and text analytics?
  • c
    How does MaxEnt address some of the limitations of traditional approaches? 2
  • d
    How does transformation-based t agging differ from other tagging approaches? 2
  • e
    Explore the concept of semantic attachments in natural language processing. 2
  • f
    How does the syntax of a language contribute to the semantic r epresentation of knowledge?
  • g
    How does the vocal tract shape affect the acoustic characteris tics of speech sounds?
  • h
    Provide examples of how different articulators contribute to speech. 2 i. Discuss the significance of using a warped frequency scale and its impact on speech feature analysis. j. How does DTW address the challenges of time misalignment in sp eech recognition, and what are its limitations?
Section BAttempt any three o f t h e f o l l o w i n g : 10 x 3 = 30
  • a
    Discuss the role of stop words in text analytics and NLP. Ho w can the identification and removal of s top words impact the quality of language processing tasks?
  • b
    Explain the concepts of interpolation and backoff in the con text of language modeling. Provide examples illustrating how these techniques en hance the performance of N-gram models
  • c
    Investigate the various types of relations that can exist be tween different senses of words. How do these relations influence the construction of semantic networks? Provide examples to illustrate your answer
  • d
    Elaborate on the Short-Time F ourier Transform method and its significance in analyzing speech signals. How does the STFT overcome challenges i n representing time-varying characteristics of speech?
  • e
    Compare and contrast Cepstral Distances, Weighted Cepstral D istances, and Filtering techniques in the context of speech feature extraction. Provide examples of scenarios where each method may be more suitable
Section CAttempt any one p a r t o f t h e f o l l o w i n g : 10 x 1 = 10
  • a
    Examine the impact of language variations and nuances on NLP applications. How do regional dialects, sla ng, and cultural differences pose challenges in developing robust natural language processing systems?
  • b
    Explore the significance of s yntactic parsing in natural lan guage processing. How does syntactic parsing contribute to the extraction of mean ingful information from sentences, and what are the common approaches used in parsing?
  • a
    Elaborate on the role of Hidde n Markov Models in part-of-speech tagging. How do HMMs model the sequence of POS tags, and what advantages do they offer over other approaches?
  • b
    Analyze the strengths and weaknesses of rule-based and stoch astic tagging methods. Provide examples to demonstrate scenarios where one approach might outperform the other
  • a
    Compare and contrast supervised methods for Word Sense Disam biguation. Discuss the challenges associated with supervised approaches an d provide examples of how the y can be effectively applied in real-world NLP tasks
  • b
    Explore the concept of bootstrapping in the context of Word Sense Disambiguation. Discuss different bootstrapping methods and their applications in improving the accuracy of WSD systems
  • a
    Provide an in-depth review of Linear Predictive Coding metho ds in speech processing. How are LPC coefficients calculated, and what role do they play in speech analysis and synthesis?
  • b
    Discuss the relationship between articulatory phonetics and acoustic phonetics in the context of speech sound production. How does the articul atory process influence the acoustic characteristics of speech?
  • a
    Investigate the role of Multiple Time-Alignment Paths in spe ech processing. How does considering multiple alignment paths contribute to the robustness and accuracy of speech recognition systems?
  • b
    Provide an in-depth comparison of LPC, PLP, and MFCC coeffic ients as feature extraction methods in speech processing. Discuss their respecti ve strengths and weaknesses in capturing relevant information from speech signals

Question text is extracted from the official AKTU question paper PDF above. Hindi translations are omitted — every question is printed in English in the original paper. Last verified: 2026-08-23.

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