Introduction to NLP

30377

Syllabus

Faculty: MANOJ KUMAR PADHI

Course Name : Introduction to NLP

Code(Credit) : CUTM3107 (0+3+1)

Course Objectives

This course introduces the concepts, Packages and techniques of natural language processing (NLP).</font
Students will gain an in-depth knowledge of the logic and computational properties of natural languages and the commonly used algorithms and libraries for processing linguistic information.

Learning Outcomes

Understand techniques and approaches to syntax and semantics in NLP.
Understand approaches to text/image/video processing, discourse, generation, dialogue and summarization within NLP.
Understand current methods for statistical approaches to machine translation.

Course Syllabus

Module-1
Introduction to Natural Language Processing , Installation and Setup of : NLTK,SPACY,GENSIM,KERAS,RASA,REGEX,SCIKITLEARN ,Python text files, PDF and regular expressions, Tokenization, Stemming, Lemmatization ,stop words , Phrase Matching and Vocabulary .

Module-2
POS and NER ,Part of speech Tagging ,Visualizing part of speech ,Visualizing NER ,Sentence Segmentation ,Text Classification ,Classification Metrics ,Confusion Matrix ,Text Feature extraction ,Semantics and Sentiment Analysis ,Semantics and word vectors ,Semantic Analysis with NLTK .

Module-3
Topic Modeling ,Latent Dirichlet Allocation Overview ,Non-negative Matrix Factorization ,Text Blob,TextBlob Introduction , Blob Word List: blob.words inTextBlob,Splitting a text in to sentences ,Generating a list of noun_phrases using TextBllob ,Easily counting proper nouns in a string using TextBlob ,Finding a polarity of a string with TextBlob ,Sentiment analysis with TextBlob ,Measuring language subjectivity with TextBlob and Python ,Language Translation with Python Module TextBlob ,TextBlob nGrams ,Spacy , Concepts and Parameters and Interacting with Chatbot

Session Plan

Session 1

Introduction to Natural Language Processing
https://www.youtube.com/watch?v=5ctbvkAMQO4
https://towardsdatascience.com/introduction-to-natural-language-processing-nlp-323cc007df3d

Session 2 , 3 & 4

Installation and Setup of : NLTK,SPACY,GENSIM,KERAS,RASA,REGEX,SCIKITLEARN
https://www.nltk.org/install.html
https://www.youtube.com/watch?v=Qu8pob9RX64
https://ashutoshtripathi.com/2020/04/02/spacy-installation-and-basic-operations-nlp-text-processing-library/
https://www.youtube.com/watch?v=GAY4fTuL60I
https://www.youtube.com/watch?v=uTDVPET8P24

Session 5

Python text files
https://www.youtube.com/watch?v=4mX0uPQFLDU
https://www.youtube.com/watch?v=Uh2ebFW8OYM
https://www.youtube.com/watch?v=AjA3DVeKRsg

Session 6 & 7

PDF and regular expressions
https://www.youtube.com/watch?v=zN8rwVXwRUE
https://www.youtube.com/watch?v=fR03-9vih2I
https://www.youtube.com/watch?v=kxHq-sq0Zm0

Session 8,9,10 & 11

Tokenization, Stemming, Lemmatization ,stop words , Phrase Matching and Vocabulary
https://stackabuse.com/python-for-nlp-tokenization-stemming-and-lemmatization-with-spacy-library/
http://Tokenization, Stemming, Lemmatization ,stop words , Phrase Matching and Vocabulary
https://www.youtube.com/watch?v=p1ccbR2P_xA
https://www.youtube.com/watch?v=uoHVztKY6S4
https://www.youtube.com/watch?v=ob6IlbV13IM

Session 12 & 13

POS and NER
https://goodboychan.github.io/chans_jupyter/python/datacamp/natural_language_processing/2020/07/17/02-Text-preprocessing-POS-tagging-and-NER.html
https://www.youtube.com/watch?v=MqQ7rqRllIc
https://www.youtube.com/watch?v=hEyEtdUmMf4

Session 14 ,15 & 16

Practice :
NLTK
SPACY

Session 17 ,18 & 19

Project Work
An autocomplete feature
Customer support bot
Predictive Text Generator
Language identifier
Media monitor
Voice Bot
Topic Modelling
Text Classification
Sentiment Analysis
Recommendation Engine

Session 20

Part of speech Tagging ,Visualising part of speech
https://www.youtube.com/watch?v=IGt8HPRARS0
https://www.youtube.com/watch?v=68hmUltbPnw

Session 21

Visualizing NER
https://www.youtube.com/watch?v=MmgjhvOSd-E
https://towardsdatascience.com/named-entity-recognition-ner-using-spacy-nlp-part-4-28da2ece57c6

Session 22,23,24 & 25

Sentence Segmentation ,Text Classification ,Classification Metrics
https://www.dataquest.io/blog/tutorial-text-classification-in-python-using-spacy/
https://www.youtube.com/watch?v=9LXq3oQEEIA
https://www.youtube.com/watch?v=N3Ep7ndcLNE
https://www.youtube.com/watch?v=hBKI7XvD8R8

Session 26

Confusion Matrix
https://towardsdatascience.com/understanding-confusion-matrix-a9ad42dcfd62
http://scikit-learn.org/stable/modules/generated/sklearn.metrics.confusion_matrix.html
https://www.youtube.com/watch?v=Kdsp6soqA7o

Session 27

Text Feature extraction
https://www.geeksforgeeks.org/feature-extraction-techniques-nlp/
https://www.youtube.com/watch?v=7YacOe4XwhY
https://www.youtube.com/watch?v=lBO1L8pgR9s

Session 28,29 & 30

Practice
Word Analysis
Word Generation
Morphology
N-Grams
N-Grams Smoothing
POS Tagging: Hidden Markov Model
POS Tagging: Viterbi Decoding
Building POS Tagger
Chunking
Building Chunker

Session 31 & 32

Project Work

Session 33 & 34

Semantics and Sentiment Analysis ,Semantics and word vectors ,Semantic Analysis with NLTK
https://builtin.com/data-science/introduction-nlp
https://towardsdatascience.com/word-embeddings-for-sentiment-analysis-65f42ea5d26e
https://www.tandfonline.com/doi/full/10.1080/19312458.2018.1455817
https://www.youtube.com/watch?v=dyN_WtjdfpA&list=PLhTjy8cBISEoOtB5_nwykvB9wfEDscuEo

Session 35

Topic Modelling
https://www.youtube.com/watch?v=p1I9Sa1lRvk
https://monkeylearn.com/blog/introduction-to-topic-modeling/

Session 36 & 37

Latent Dirichlet Allocation Overview ,Non-negative Matrix Factorisation
https://towardsdatascience.com/topic-modeling-quora-questions-with-lda-nmf-aff8dce5e1dd
https://www.youtube.com/watch?v=dukbU0pVD1g
https://www.youtube.com/watch?v=o4pPTwsd-5M

Session 38

Text Blob, TextBlob Introduction
https://www.youtube.com/watch?v=Cxzyalrjr94
https://www.analyticsvidhya.com/blog/2018/02/natural-language-processing-for-beginners-using-textblob/

Session 39 & 40

Blob Word List: blob.words inTextBlob,Splitting a text in to sentences
https://textblob.readthedocs.io/en/dev/
https://www.youtube.com/watch?v=UyB0JbJDBpM
https://www.youtube.com/watch?v=ea4IadDRwuc
https://www.youtube.com/watch?v=Cxzyalrjr94&list=PL_92WMXSLe_-RkWW5zAQZ-gMdVqZ7T-_F

Session 41

Practice
Avinash wants to help his sister mounika to pass through the aptitude exam, so in order to help her he wants to test her skills in english .So he fixed to assign some sentences and want the key root words in the sentences. So write a python code that helps mounika to get the root words in the given sentences?(Note:Use stemming and tokenization process)
Avinash is a comic editor in a X company one day while editing a particular script he became enthusiastic about the story of the script so there is no time to read the whole script he decided to understand the total story line by learning about the characters so he wants to separate the words in the sentence to know the characters as the whole story is complex ,So write a python program that splits the words and display both splitted words and count of the words in the given sentence using tokenizer function?
Avinash is a English teacher in a school.She wants to teach the students about connectors and prepositions and there usage in sentence formation so she thought an idea that helps students to understand more about the connectors and prepositions so he planned to give different sentences to the students and to remove the connectors and prepositions in the sentence .Write a python code to help students to remove those connectors and prepositions?(Note: connectors and prepositions represents stop words take them in a text file for required output)