Natural Language Processing
Natural Language Processing. Instructor: Prof. Pawan Goyal, Department of Computer Science and Engineering, IIT Kharagpur. This course deals with various topics in natural language processing and its applications: basic text processing, spelling correction, language modeling, advanced smoothing for language modeling, Part of Speech tagging, models for sequential tagging, syntax, constituency parsing, dependency parsing, lexical semantics, distributional semantics, topic models, entity linking, information extraction, text summarization, text classification, sentiment analysis and opinion mining.
(from nptel.ac.in )

Lecture 57 - Tokenization and Pre-processing for Sentiment Analysis
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Lecture 01 - Introduction
Lecture 02 - NLP Applications: Machine Translation, Sentiment Analysis
Lecture 03 - Why is NLP Hard? - Ambiguities in Language
Lecture 04 - Empirical Laws: Heap's Law, Zipf's Law, Type-Token Ratio
Lecture 05 - Text Processing: Word toKenization and Segmentation, Lemmatization, Stemming
Lecture 06 - Spelling Correction: Edit Distance - Dynamic Programming Approach
Lecture 07 - Weighted Edit Distance, Finding Dictionary Entries with Small Edit Distances
Lecture 08 - Noisy Channel Model for Spelling Correction
Lecture 09 - N-Gram Language Model
Lecture 10 - Evolution of Language Models, Basic Smoothing
Lecture 11 - Language Modeling: Advanced Smoothing Models
Lecture 12 - Computational Morphology
Lecture 13 - Finite State Methods for Morphology
Lecture 14 - Introduction to Part of Speech Tagging
Lecture 15 - Hidden Markov Models for PoS Tagging
Lecture 16 - Viterbi Decoding for Hidden Markov Models, Parameter Learning
Lecture 17 - Forward-Backward Algorithm, Baum Welch Algorithm
Lecture 18 - Maximum Entropy Models I
Lecture 19 - Maximum Entropy Models II: Maximum Entropy Markov Model, Beam Search
Lecture 20 - Conditional Random Fields
Lecture 21 - Syntax - Introduction
Lecture 22 - Syntax - Parsing I
Lecture 23 - Syntax - CKY Algorithm, PCFGs
Lecture 24 - PCFGs - Inside-Outside Probabilities
Lecture 25 - Inside-Outside Probabilities
Lecture 26
Lecture 27 - Dependency Grammars and Parsing - Introduction
Lecture 28 - Transition based Parsing: Formulation
Lecture 29 - Transition based Parsing: Learning
Lecture 30 - Maximum Spanning Tree (MST) based Dependency Parsing
Lecture 31 - MST based Dependency Parsing: Learning
Lecture 32 - Distributional Semantics - Introduction
Lecture 33 - Distributional Models of Semantics
Lecture 34 - Distributional Semantics: Applications, Structured Models
Lecture 35 - Word Embeddings, Part I
Lecture 36 - Word Embeddings, Part II
Lecture 37 - Lexical Semantics
Lecture 38 - Lexical Semantics - Wordnet
Lecture 39 - Word Sense Disambiguation: Lesk Algorithm, Random Walk Approach, Naive Bayes
Lecture 40 - Word Sense Disambiguation: Semi-Supervised and Unsupervised Approaches
Lecture 41 - Novel Word Sense Detection
Lecture 42 - Topic Models - Introduction
Lecture 43 - Latent Dirichlet Allocation: Formulation
Lecture 44 - Gibbs Sampling for LDA, Applications
Lecture 45 - LDA Variants and Applications: Correlated Topic Models, Dynamic Topic Models, Supervised LDA
Lecture 46 - LDA Variants and Applications: Relational Topic Models, Bayesian Nonparametrics
Lecture 47 - Entity Linking: Wikification, Mention Detection, Link Disambiguation, Key Phraseness
Lecture 48 - Entity Linking: Relatedness, Learning to Link
Lecture 49 - Information Extraction: Definition and Applications, Regex, Hand-built Patterns
Lecture 50 - Bootstrapping and Supervised Relation Extraction
Lecture 51 - Distort Supervision, Freebase, Syntactic Dependency Paths
Lecture 52 - Text Summarization - Concepts, Lexrank, Maximal Marginal Relevance
Lecture 53 - Optimization based Approaches for Summarization
Lecture 54 - Summarization Evaluation: Manual Evaluation, Rouge Evaluation
Lecture 55 - Text Classification: Naive Bayes, Bag of Words, Add One Smoothing
Lecture 56 - Text Classification: Naive Bayes, Multi-value Classification, Confusion Matrix
Lecture 57 - Tokenization and Pre-processing for Sentiment Analysis
Lecture 58 - Sentiment Analysis - Affective Lexicons
Lecture 59 - Learning Affective Lexicons
Lecture 60 - Computing with Affective Lexicons
Lecture 61 - Aspect based Sentiment Analysis