Lecture 1: Introduction to the Course
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1. The translated content of this course is available in regional languages. For details please visit https://nptel.ac.in/translation
The video course content can be accessed in ...the form of regional language text transcripts, books which can be accessed under downloads of each course, subtitles in the video and Video Text Track below the video.
Your feedback is highly appreciated. Kindly fill this form https://forms.gle/XFZhSnHsCLML2LXA6
2. Regional language subtitles available for this course
To watch the subtitles in regional languages:
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2. Play the video.
3. Now click on the Settings icon and a list of features will display
4. From that select the option Subtitles/CC.
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Lecture 1: Introduction to the Course
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Lecture 2: What Do We Do in NLP
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Lecture 3: Why is NLP hard
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Lecture 4: Empirical Laws
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Lecture 5: Text Processing: Basics
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Lecture 6: Spelling Correction: Edit Distance
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Lecture 7: Weighted Edit Distance, Other Variations
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Lecture 8: Noisy Channel Model for Spelling Correction
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Lecture 9 : N-Gram Language Models
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Lecture 10: Evaluation of Language Models, Basic Smoothing
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Week 2 Tutorial
Lecture 12: Language Modeling: Advanced Smoothing Models
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Lecture 13: Computational Morphology
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Lecture 14: Finite - State Methods for Morphology
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Lecture 15: Introduction to POS Tagging
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Lecture 16: Hidden Markov Models for POS Tagging
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Lecture 17: Viterbi Decoding for HMM, Parameter Learning
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Lecture 18: Baum Welch Algorithm
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Lecture 19: Maximum Entropy Models - I
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Lecture 20: Maximum Entropy Models - II
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Lecture 21: Conditional Random Fields
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Lecture 22: Syntax - Introduction
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Lecture 23: Syntax - Parsing I
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Lecture 24: Syntax - CKY, PCFGs
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Lecture 25: PCFGs - Inside-Outside Probabilities
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Lecture 26: Inside-Outside Probabilities
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Lecture 27: Dependency Grammars and Parsing - Introduction
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Lecture 28 : Transition Based Parsing : Formulation
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Lecture 29 : Transition Based Parsing : Learning
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Lecture 30 : MST-Based Dependency Parsing
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Lecture 31: MST-Based Dependency Parsing : Learning
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Lecture 32: Distributional Semantics - Introduction
Lecture 33: Distributional Models of Semantics
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Lecture 34: Distributional Semantics : Applications, Structured Models
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Lecture 35: Word Embeddings - Part I
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Lecture 36 : Word Embeddings - Part II
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Lecture 37: Lexical Semantics
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Lecture 38: Lexical Semantics - Wordnet
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Lecture 39 : Word Sense Disambiguation - I
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Lecture 40 : Word Sense Disambiguation - II
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Lecture 41 : Novel Word Sense detection
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Lecture 42 : Topic Models : Introduction
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Lecture 43 :Latent Dirichlet Allocation : Formulation
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Lecture 44 : Gibbs Sampling for LDA, Applications
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Lecture 45 : LDA Variants and Applications - I
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Lecture 46:LDA Variants and Applications - II
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mod10lec47
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mod10lec48
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mod10lec49
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mod10lec50
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