ABOUT THE COURSE
In the ever-evolving landscape of NLP, the past few years have marked a paradigm shift driven by revolutionary advancements in large pre-trained models employing cutting-edge deep-learning techniques. Among these, generative models like GPT-3 have emerged as trailblazers, opening doors to a new era in creating intelligent systems that can comprehend text and generate human-like responses. This course offers learners a profound understanding of the latest developments in NLP and information retrieval. From exploring the intricacies of state-of-the-art algorithms to gaining hands-on experience with practical applications, participants will be equipped with the knowledge and skills needed to navigate and contribute to the dynamic field of natural language processing.
COURSE OUTCOMES
CO - 1: Understand the fundamental concepts, historical development, linguistic essentials, and text preprocessing techniques in NLP.
CO - 2: Apply various machine learning algorithms and text representations to perform sentiment analysis and text classification tasks.
CO - 3: Explore deep learning models and Transformer-based architectures to tackle complex NLP tasks, such as sentiment analysis and language generation
CO - 4: Develop practical skills in building information retrieval systems, question-answering models, and text summarization while considering ethical implications in NLP.
COURSE MODULES
MODULE 1: Foundations of Natural Language Processing - Introduction to NLP: Definition, Scope, and Historical Background, Linguistic Essentials for NLP: Phonetics, Phonology, Morphology, Syntax, Semantics, and Pragmatics, Text Pre-processing: Tokenization, Stemming, and Lemmatization, Stop word removal, Part-of-speech tagging, Named Entity Recognition (NER), Language Modeling: N-grams, Hidden Markov, Models (HMM), Introduction to neural language models.
MODULE 2: Natural Language Processing Techniques - Machine Learning for NLP: Supervised, unsupervised, and semi-supervised learning in NLP, Feature extraction for text data, Sentiment analysis as a classification problem, Text Classification: Naive Bayes Classifier, Support Vector Machines, Neural Network for Text Classification (CNN, RNN), Language Understanding: Introduction to Word Embeddings (Word2Vec and Glove), Distributional Semantics and Word Similarity, Text Representation using TF-IDF, Sequence-to-Sequence Models, Attention Mechanisms, Applications
MODULE 3: Advanced NLP Topics - Deep Learning for NLP: Transformer-based models (BERT, GPT, XLNet), Fine-tuning pre-trained models, Sentiment Analysis and Emotion Recognition: Aspect-based Sentiment Analysis, Detecting emotions from text using deep learning, Named Entity Recognition and Entity Linking, Entity Linking with knowledge bases, Natural Language Generation: Text Generation with Recurrent Neural Networks, Introduction to Generative Adversarial Networks (GANs) for text.
MODULE 4: NLP Applications and Ethics - Information retrieval models: Boolean Retrieval, Vector Space Models, Evaluation Metrics, Question Answering Systems: QA pipelines, Reading comprehension with attention-based models, Text Summarization: Extractive vs. Abstractive Summarization, Sequence-to-Sequence models for summarization, Ethics, and Bias in NLP: Addressing bias in language models, Ethical considerations in NLP applications, Responsible use of NLP in society
COURSE PREREQUISITES
Knowledge on Python programming is a strong prerequisite for this course. Also, it is nice to have basic math and statistics skills as well. Knowledge of using Git, Jupyter Lab, and/or Google Colab is highly appreciated but not mandatory. It would be really nice if the students are familiar with object-oriented programming, simple data structures such as hash maps, and text processing.
TEXTBOOKS
Speech and Language Processing by Daniel Jurafsky and James H. Martin
Natural Language Processing with Python by Steven Bird, Ewan Klein, and Edward Loper
Natural Language Processing in Action by Lane, Howard, and Hapke
Deep Learning for Natural Language Processing by Palash Goyal, Sumit Pandey, and Karan Jain
Practical Natural Language Processing by Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana
LEARNING MATERIALS
TBD
LAB EXERCISES
TBD
ASSIGNMENTS
TBD
VIDEO LECTURES
Introduction to NLP - I
Introduction to NLP - II
Introduction to Regular Expression and Tokenization
Introduction to Stemming and Lemmatization
Introduction to Machine Translation
Introduction to Knowledge Acquisition
Introduction to Text Classification
Introduction to Sentiment Analysis
CONTACT
If you have any questions or need further clarification about the course, please contact me via email or during my office hours. Let's embark on this exciting journey into natural language processing! Get ready to unlock the potential of AI through the power of NLP.
Looking forward to a rewarding and knowledge-filled semester!
Dr. Anoop V. S.
Course Instructor