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Natural Language Processing

Natural Language Processing. Instructor: Prof. Pushpak Bhattacharyya, Department of Computer Science and Engineering, IIT Bombay. This course provides an understanding of natural language processing, its tools, techniques, philosophy and principle. Topics covered include sound, words and word forms, structures, meaning, and web 2.0 applications:

Sound: Biology of Speech Processing; Place and Manner of Articulation; Word Boundary Detection; Argmax based computations; HMM and Speech Recognition.
Words and Word Forms: Morphology fundamentals; Morphological Diversity of Indian Languages; Morphology Paradigms; Finite State Machine Based Morphology; Automatic Morphology Learning; Shallow Parsing; Named Entities; Maximum Entropy Models; Random Fields.
Structures: Theories of Parsing, Parsing Algorithms; Robust and Scalable Parsing on Noisy Text as in Web documents; Hybrid of Rule Based and Probabilistic Parsing; Scope Ambiguity and Attachment Ambiguity resolution.
Meaning: Lexical Knowledge Networks, Wordnet Theory; Indian Language Wordnets and Multilingual Dictionaries; Semantic Roles; Word Sense Disambiguation; WSD and Multilinguality; Metaphors; Coreferences.
Web 2.0 Applications: Sentiment Analysis; Text Entailment; Robust and Scalable Machine Translation; Question Answering in Multilingual Setting; Cross Lingual Information Retrieval (CLIR). (from nptel.ac.in)

Lecture 31 - Wordnet; Metonymy and Word Sense Disambiguation


Go to the Course Home or watch other lectures:

Lecture 01 - Introduction
Lecture 02 - Stages of NLP
Lecture 03 - Stages of NLP (cont.)
Lecture 04 - Two Approaches to NLP
Lecture 05 - Sequence Labelling and Noisy Channel
Lecture 06 - Noisy Channel: Argmax based Computation
Lecture 07 - Argmax based Computation
Lecture 08 - Noisy Channel Application to NLP
Lecture 09 - Probabilistic Parsing, Part of Speech Tagging
Lecture 10 - Part of Speech Tagging
Lecture 11 - Part of Speech Tagging (cont.)
Lecture 12 - Part of Speech Tagging (cont.), Indian Language in Focus; Morphology Analysis
Lecture 13 - PoS Tagging (cont.), Indian Language Consideration; Accuracy Measure
Lecture 14 - PoS Tagging: Fundamental Principle; Why Challenging; Accuracy
Lecture 15 - PoS Tagging; Accuracy Measurement; Word Categories
Lecture 16 - Artificial Intelligence and Probability; Hidden Markov Model (HMM)
Lecture 17 - Hidden Markov Model (HMM)
Lecture 18 - HMM, Viterbi, Forward-Backward Algorithm
Lecture 19 - HMM, Viterbi, Forward-Backward Algorithm (cont.)
Lecture 20 - HMM, Viterbi, Forward-Backward Algorithms, Baum-Welch Algorithm
Lecture 21 - HMM, Viterbi, Forward-Backward Algorithms, Baum-Welch Algorithm (cont.)
Lecture 22 - Natural Language Processing and Information Retrieval
Lecture 23 - Cross Lingual Information Access (CLIA); Information Retrieval (IR) Basics
Lecture 24 - IR Models: Boolean Vector
Lecture 25 - IR Models: NLP and IR Relationship
Lecture 26 - NLP and IR: How NLP has used IR, toward Latent Semantic Indexing-PCA
Lecture 27 - Least Squares Method; Recap of PCA; Towards Latent Semantic Indexing
Lecture 28 - PCA; SVD; Towards Latent Semantic Indexing
Lecture 29 - Wordnet and Word Sense Disambiguation
Lecture 30 - Wordnet and Word Sense Disambiguation (cont.)
Lecture 31 - Wordnet; Metonymy and Word Sense Disambiguation
Lecture 32 - Word Sense Disambiguation
Lecture 33 - Word Sense Disambiguation: Overlap based Method; Supervised Method
Lecture 34 - Word Sense Disambiguation: Supervised and Unsupervised Methods
Lecture 35 - Word Sense Disambiguation: Semi-Supervised and Unsupervised Methods; Resource-Constrained WSD
Lecture 36 - Resource-Constrained WSD; Parsing
Lecture 37 - Parsing
Lecture 38 - Parsing Algorithm
Lecture 39 - Parsing Ambiguous Sentences; Probabilistic Parsing
Lecture 40 - Probabilistic Parsing Algorithms