315329: Natural Language Processing Syllabus for Artificial Intelligence 5th Sem K Scheme MSBTE PDF

Natural Language Processing detailed Syllabus for Artificial Intelligence (AI), K scheme PDF has been taken from the MSBTE official website and presented for the diploma students. For Subject Code, Subject Name, Lectures, Tutorial, Practical/Drawing, Credits, Theory (Max & Min) Marks, Practical (Max & Min) Marks, Total Marks, and other information, do visit full semester subjects post given below.

For all other MSBTE Artificial Intelligence 5th Sem K Scheme Syllabus PDF, do visit MSBTE Artificial Intelligence 5th Sem K Scheme Syllabus PDF Subjects. The detailed Syllabus for natural language processing is as follows.

Rationale

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Course Outcomes:

Students will be able to achieve & demonstrate the following COs on completion of course based learning

1. Explain key concepts linguistics and NLP.
2. Implement Text Normalization and Text Preprocessing techniques to the text.
3. Apply Part Of Speech ,Parsing ,Named Entity Recognition techniques to the text.
4. Generate text embedding in NLP.
5. Use Transformer in NLP applications.

Unit I

Natural Language Basics 1.1 Overview of NLP ,The need of NLP, Areas of study under linguistics 1.2 Language Syntax and Structure: Words, Phrases, Clauses, Grammar, Word Order Typology, Word Order-Based Language Classification 1.3 Language Semantics: Lexical Semantic Relations,Semantic Networks and Models 1.4 Text Corpora:Corpora Annotation and Utilities, Popular Corpora 1.5 Applications of NLP.

Suggested Learning Pedagogie
Lecture Using Chalk-Board Presentations

Unit II

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Unit III

Text Syntax and Structure 3.1 Part-of-speech tagging : English Word Classes ,Part-of-Speech Tagging 3.2 Named entity recognition :Named Entities and Named Entity Tagging ,IOB/ BIO tagging 3.3 Parsing Techniques :Partial parsing/chunking ,Dependency parsing

Suggested Learning Pedagogie
Lecture Using Chalk-Board Demonstration Hands-on

Unit IV

Text Feature Extraction 4.1 Vector Space Models :Words and Vectors , Cosine for measuring similarity 4.2 One hot encoding ,Bag-of-words ,TF-IDF 4.3 Word2vec:continuous bag of words, skip gram 4.4 Contextual Embeddings : Contextual Embeddings ,Word Sense

Suggested Learning Pedagogie
Lecture Using Chalk-Board Demonstration Hands-on

Unit V

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List of Experiments:

1. Implement a program to use text corpus. i)Brown corpus, ii)Penn Treebank Corpus. 2
2. i)Write program for Sentence Segmentation Techniques. ii)Write program Word Segmentation using Re and nltk. 2
3. Implement Penn Treebank tokenization, word_tokenize,wordpunct_tokenize,sent_tokenize,WhitespaceTokenizer. 2
4. *Apply various Lemmatization Techniques and Stemming Techniques such as porter stemmer,Lancaster Stemmer,Stemmer on text. 2
5. *Write program on Text Normalization using nltk: i)Tokenizing text ii)Removing special charactersiii)contractions iv)case conversion. ii)*Generate unigram, bigram, trigram for given text. 2
6. *Write program for POS tagging on the given text. 2
7. *Write program to find Named Entity Recognition(NER) for the given text . 2
8. i)Implement a program for dependency parse tree on the sentence using nltk or spacy. ii)Write program performing chunking on the given text .Extract Noun Phrases, Verb Phrases, Adjective Phrases. 2
9. *Write program to generate word embedding using word2Vec and BERT embedding(use Hugging Face). 2
10. Perform the prediction task using NLP and ML classifiers: a)Sentiment analysis b) Fake news detection. 2
11. * Implement program to fine-tune a pre-trained model from Hugging Face’s for text classification. 2

Self Learning

Micro Project

1. Sentiment Analysis – Develop a model to classify text as positive, negative, or neutral using NLP techniques. 2)Fake News Detection – Train a classifier to differentiate between real and fake news articles based on linguistic patterns. 3)Keyword Extraction – Extract the most relevant keywords from a document using NLP algorithms like TF-IDF or RAKE.

Laboratory Equipment

For the complete Syllabus, results, class timetable, and many other features kindly download the iStudy App
It is a lightweight, easy to use, no images, and no pdfs platform to make students’s lives easier.
.

Learning Materials

1. Daniel Jurafsky Speech and Language Processing -ch2 2.1,2.3,2.4 ch3,ch4 Pearson Publication ISBN :978-0131873216
2. Dipanjan Sarkar Text Analytics with Python ch1 ch5 5.1 Apress ISBN-13 (pbk): 978-1-4842-2387-1
3. Steven Bird, Ewan Klein, and Edward Loper Natural Language Processing with python ch5 5.2 5.3 5.4 Oreally ISBN:978-0-596-51649-9
4. Akshay Kulkarni Adarsha Shivananda Natural Language Processing Recipes_ Unlocking Text Data with Machine Learning and Deep Learning using Python . for lab 1 to 11 Apress ISBN-13 (pbk): 978-1-4842-4266-7
5. Pushpak Bhattacharyya and Aditya Joshi Natural Language Processing ch2-2.5 ch3-3.3 Willey ISBN:978-93-5746-238-9

Learning Websites

1. https://web.stanford.edu/~jurafsky/slp3/ NLP e-book and PPT
2. https://github.com/Donges-Niklas/Intro-to-NLP-with-NLTK/blob /master/nltk.ipynb Text Segmentation, Stop Words & Word Segmentation, Stemming ,Parsing (Speech Tagging & Chunking),programs
3. https://github.com/samiramunir/Simple-Sentiment-Analysis-usi ng-NLTK/blob/master/live_classifier.py sentiment Analysis Program
4. https://www.youtube.com/watch? v=yLDRHyNJSXA&list=PLPIwNooIb9 vimsumdWeKF3BRzs9tJ-_gy&index=38 Sentiment Analysis theory content
5. https://www.youtube.com/watch? v=fM4qTMfCoak&list=PLZoTAELRMX VMdJ5sqbCK2LiM0HhQVWNzm NLP concept playlist

For detail Syllabus of all other subjects of Artificial Intelligence, K scheme do visit Artificial Intelligence 5th Sem Syllabus for K scheme.

For all Artificial Intelligence results, visit MSBTE Artificial Intelligence all semester results direct links.