Syllabus

Introduction, Mathematical Preliminaries [2hrs]
Basic Text Processing, Edit distance [2hrs]
Linear Text Classification (NB,LR) [4hrs]
Word Embeddings [3hrs]
Language models, spelling correction [3hrs]
Neural Networks and Neural Language Models [3hrs]
Deep Learning Architectures for Sequence Processing [3hrs]
Sequence labelling POS tagging, NER, Tokenization [3hrs]
Parsing [3hrs]
Machine Translation [4hrs]
Semantics [4hrs]
Reference resolution, Discourse (Entity Linking) [2hrs]
Information/Relation Extraction [1hrs]
Question Answering [1hrs]
Summarization [1hrs]
Dialogue Systems [1hrs]
Sentiment Analysis [2hrs]

to understand the basics of language processing

to learn about language models, sequence labelling tasks

to learn about parsing, machine translation systems

to learn about Q/A systems, Summarization, Chatbots

Students will learning the following:
1. How a language model works
2. How a POS tagging system, NER systems works
3. How to design a Parser for a given language
4. How to design a statistical as well as neural machine translation model
5. How a Q/A system and Chatbot works
6. How a summarization model works

(Daniel Jurafsky and James Martin, Speech and Language Processing, Prentice-Hall , Second Edition, 2008 <br />ISBN: 0131873210

Christopher Manning and Hinrich Schutze, Foundations of Statistical Natural Language Processing, MIT Press , 1999

Deepti Chopra, Jacob Perkins, and Nitin Hardeniya, Natural Language Processing: Python and NLTK, packt

Dipanjan Sarkar, Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from Your Data, Apress