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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 61 - Aspect based 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