CS-626: Speech and Natural Language Processing and the Web

Announcement

• Join the MS Teams for attending live lectures
• The third assignment is uploaded on teams, moodle and course page.

---

Course Details

CS626: Speech and Natural Language Processing and the Web
Department of Computer Science and Engineering
Indian Institute of Technology Bombay

Time Table and Venue

• Monday: 8:30 AM to 9:25 AM
• Tuesday: 9:35 AM to 10:30 AM
• Thursday: 10:35 AM to 11:30 AM

Course Content

The general approach in the course will be covering (i) a language phenomenon, (ii) the corresponding language processing task, and (iii) techniques based on deep learning, classical machine learning and knowledge base. On one hand we will understand the language processing task in detail using linguistics, cognitive science, utility etc., on the other hand we will delve deep into techniques for solving the problem. The topics are given now.

• Sound: Biology of Speech Processing; Place and Manner of Articulation; Peculiarities of Vowels and Consonants; Word Boundary Detection; Argmax based computations; Hidden Markov Model and Speech Recognition; deep neural nets for speech processing.
  
• Morphology: Morphology fundamentals; Isolating, Inflectional, Agglutinative morphology; Infix, Prefix and Postfix Morphemes, Morphological Diversity of Indian Languages; Morphology Paradigms; Rule Based Morphological Analysis: Finite State Machine Based Morphology; Automatic Morphology Learning; Deep Learning based morphology analysis.
  
• Shallow Parsing: Part of Speech (POS) Tagging; HMM based POS tagging; Maximum Entropy Models and POS; Random Fields and POS; DNN for POS.
  
• Parsing: Constituency and Dependency Parsing; Theories of Parsing; Scope Ambiguity and Attachment Ambiguity Resolution; Rule Based Parsing Algorithms; Probabilistic Parsing; Neural Parsing.
  
• Meaning: Lexical Knowledge Networks, Wordnet Theory and Indian Language Wordnets; Semantic Roles; Word Sense Disambiguation; Metaphors.
  
• Discourse and Pragmatics: Coreference Resolution; Cohesion and Coherence.
  
• Applications: Machine Translation; Sentiment and Emotion Analysis; Text Entailment; Question Answering; Code Mixing; Analytics and Social Networks, Information Retrieval and Cross Lingual Information Retrieval (IR and CLIR)
  

Pre-requisites

Data Structures and Algorithms, Python (or similar language) Programming skill

---

References

• Allen, James, Natural Language Understanding, Second Edition, Benjamin/Cumming, 1995.
• Charniack, Eugene, Statistical Language Learning, MIT Press, 1993
• Jurafsky, Dan and Martin, James, Speech and Language Processing, Speech and Language Processing (3rd ed. draft), Draft chapters in progress, October 16, 2019.
• Manning, Christopher and Heinrich, Schutze, Foundations of Statistical Natural Language Processing, MIT Press, 1999.
• Jacob Eisenstein, Introduction to Natural Language Processing, MIT Press, 2019.
• Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT Press, 2016.
• Radford, Andrew et. al., Linguistics, an Introduction, Cambridge University Press, 1999.
• Pushpak Bhattacharyya, Machine Translation, CRC Press, 2017.
• Journals: Computational Linguistics, Natural Language Engineering, Machine Learning, Machine Translation, Artificial Intelligence
• Conferences: Annual Meeting of the Association of Computational Linguistics (ACL), Computational Linguistics (COLING), European ACL (EACL), Empirical Methods in NLP (EMNLP), Annual Meeting of the Special Interest Group in Information Retrieval (SIGIR), Human Language Technology (HLT).