Natural Language Processing detailed syllabus scheme for Information Technology (IT), 2020-21 onwards has been taken from the DBATU official website and presented for the Bachelor of Technology students. For Subject Code, Course Title, Lecutres, Tutorials, Practice, Credits, and other information, do visit full semester subjects post given below.
For 7th Sem Scheme of Information Technology (IT), 2020-21 Onwards, do visit IT 7th Sem Scheme, 2020-21 Onwards. For the Elective-VIII scheme of 7th Sem 2020-21 onwards, refer to IT 7th Sem Elective-VIII Scheme 2020-21 Onwards. The detail syllabus for natural language processing is as follows.
Natural Language Processing Syllabus for Information Technology (IT) 4th Year 7th Sem 2020-21 DBATU
Course Objectives:
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Course Outcomes:
After learning the course the student will be able:
- To understand the models, methods, and algorithms of statistical Natural Language Processing
- To implement probabilistic models in code, estimate parameters for such models, and run meaningful experiments to validate such models.
- To apply core computer science concepts and algorithms, such as dynamic programming.
- To understand linguistic phenomena and explore the linguistic features relevant to each NLP task.
- To identify opportunities and conduct research in NLP
- To analyze experimental results and write reports
Unit I
introduction to NLP: Definition, issues and strategies, application domain, tools for NLP, Linguistic organization of NLP, NLP vs PLP.
Unit II
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Unit III
Phonology: Speech sounds, phonetic transcription, phoneme and phonological rules, optimality theory, machine learning of phonological rules, phonological aspects of prosody and speech synthesis. Pronunciation, Spelling and N-grams: Spelling errors, detection and elimination using probabilistic models, pronunciation variation (lexical, allophonic, dialect), decision tree model, counting words in Corpora, simple N-grams, smoothing (Add one, Written-Bell, Good-Turing), N-grams for spelling and pronunciation.
Unit IV
Syntax: POS Tagging: Tagsets, concept of HMM tagger, rule based and stochastic POST, algorithm for HMM tagging, transformation based tagging. Sentence level construction and unification: Noun phrase, co-ordination, sub-categorization, concept of feature structure and unification.
Unit V
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Unit VI
Pragmatics: Discourse: Reference resolution and phenomena, syntactic and semantic constraints on coreference, pronoun resolution algorithm, text coherence, discourse structure. Dialogues: Turns and utterances, grounding, dialogue acts and structures. Natural Language Generation: Introduction to language generation, architecture, discourse planning (text schemata, rhetorical relations)
Text Books:
- D. Jurafsky and J. H. Martin – Speech and Language Processing – An introduction to Language processing, Computational Linguistics, and Speech Recognition, Pearson Education
- Allen, James. 1995. – Natural Language Understanding. Benjamin/Cummings, 2ed.
Reference Book:
- Bharathi, A., Vineet Chaitanya and Rajeev Sangal. 1995. Natural Language Processing-A Pananian Perspective. Prentice Hall India, Eastern Economy Edition.
- Eugene Cherniak, Statistical Language Learning, MIT Press, 1993.
- Manning, Christopher and Heinrich Schutze, Foundations of Statistical Natural Language Processing. MIT Press, 1999.
For detail syllabus of all subjects of Information Technology (IT) 7th Sem 2020-21 onwards, visit IT 7th Sem Subjects of 2020-21 Onwards.