AD 802(A) Natural language Processing

Artificial Intelligence & Data Science, VIII-Semester

Departmental Elective- AD-802 (A) Natural Language Processing

Syllabus

Unit I: Introduction: Natural Language Processing(NLP): Definition and scope Applications in various domains, Challenges and limitations. NLP tasks in syntax, semantics, and pragmatics. Different Data Models such as Boolean Model, Vector model, Probabilistic Model. Comparison of classical NLP models: Rule-based model, Statistical model, Information retrieval model, Rule-based machine translation model, Probabilistic Graphical model.

Unit II: Linguistics and Morphology: Linguistic essentials: Phonetics and Phonology, Morphology: Morphens, Syntax, Semantics, Pragmatics, Semiotics, Discourse Analysis, Psycholinguistics, Corpus Linguistics. Word Formation Processes. Morphological analysis. Morphological Finite State Transducers.

Unit III: Word Level Analysis: Tokenization, Part-of-Speech Tagging (POS Tagging), Lemmatization, Stemming, Named Entity Recognition (NER), Word Sense Disambiguation (WSD), Word Embedding. Types of PoS Tagging: Rule-based, Stochastic, Transformation- based, Lexical. Hidden Markov model and Maximum Entropy model. n-grams. Collocations. Applications of NER.

Unit IV: Syntax Analysis: Grammatical Formalisms: Context Free Grammars, Grammar rules for English, Syntactic parsing: Grammar formalisms and treebanks. Efficient parsing for context-free grammars (CFGs). Statistical parsing and probabilistic CFGs (PCFGs).

Unit V: Semantic Analysis: Requirements for representation, First-Order Logic, Description Logics. Syntax-Driven Semantic analysis, Semantic attachments. Word Senses, Relations between Senses, Thematic Roles, selectional restrictions. Word-sense disambiguation(WSD): WSD using Supervised, Dictionary and Thesaurus. Applications of WSD.

Text Books:

1. Daniel Jurafsky& James H. Martin, Speech and Language Processing, Perason publication,2018.

2. Steven Bird, Ewan Klein and Edward Loper, ―Natural Language Processing with Python, First Edition, OReilly Media, 2009.

3. Nitin Indurkhya-. Handbook of natural language processing. Chapman and Hall/CRC, 2010.

4. Manning and Schutze "Foundations of Statistical Natural Language Processing", MIT Press,2009

Reference Books:

1. Dipanjan Sarkar- Text Analytics with Python. Apress/Springer, 2016

2. Rothman, Denis, and Antonio Gulli- Transformers for Natural Language Processing: Build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3. Packt Publishing Ltd, 2022.

3. Tanveer Siddiqui, U.S. Tiwary, ―Natural Language Processing and Information Retrieval, Oxford University Press, 2008

Notes

• notes Unit-1 AD-802 (A) NLP

• Notes Unit-2 AD-802 (A) NLP

• Notes Unit-3 AD-802 (A) NLP

• Notes Unit-4 AD-802 (A) NLP 

• Notes Unit-5 AD-802 (A) NLP 

Assignment

• NLP assignment Unit-3 AD_802(A)

• NLP assignment Unit-5 AD_802(A)

• NLP assignment Unit-1 AD_802(A)

• NLP assignment Unit-2 AD_802(A)

• NLP assignment Unit-4 AD_802(A)

AD-804 Departmental Elective_lab manual