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Natural Language Processing
broucher Course Brochure
Natural Language Processing
by Prof Pushpak Bhattacharyya

Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data....

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level All Level
level English
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Program Fee
FREE
About This Course

Natural language processing (NLP) is a subfield of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data....

Course Curriculum
1 Sections | 39 Lecture | 30h 21min total length
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Natural Language Processing

Natural Language Processing

| 30h 21min total length
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    L9-Brief on Probabilistic Parsing & Start of Part of Speech Tagging

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    L8-Noisy Channel Application to NLP

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    L7-Argmax Based Computation

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    L6-Noisy Channel: Argmax Based Computation

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    L5-Sequence Labelling and Noisy Channel

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    L4-Two approaches to NLP

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    L2-Stages of NLP

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    L1-Introduction

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    L10-Part of Speech Tagging

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    L12-Part of Speech Tagging counted & Indian Language in Focus; Morphology Analysis

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    L11-Part of Speech Tagging counted

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    L13-PoS Tagging contd , Indian Language Consideration; Accuracy Measure

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    L14-PoS Tagging; Fundamental Principle; Why Challenging; accuracy

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    L15-PoS Tagging; Accuracy Measurement; Word categories

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    L20-HMM, Forward Backward Algorithms, Baum Welch Algorithm

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    L19-HMM, Viterbi, Forward Backward Algorithm Contd..

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    L18-HMM, Viterbi, Forward Backward Algorithm

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    L17-HMM

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    L16-AI and Probability; HMM

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    L30-Wordnet and Word Sense Disambiguation(contd)

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    L29-Wordnet and Word Sense Disambiguation?

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    L28-PCA; SVD; Towards Latent Semantic Indexing(LSI)

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    L27-Least Square Method; Recap of PCA; Towards Latent Semantic Indexing(LSI)

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    L26-NLP and IR: How NLP has used IR, Toward Latent Semantic

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    L25-IR Models: NLP and IR Relationship

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    L24-IR Models: Boolean Vector

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    L23-CLIA; IR Basics

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    L22-Natural Language Processing and Informational Retrieval

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    L21-HMM, Forward Backward Algorithms, Baum Welch Algorithm Contd...

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    L40-Probabilistic Parsing Algorithms

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    L39-Parsing Ambiguous Sentences; Probabilistic Parsing

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    L38-Parsing Algorithm

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    L37-Parsing

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    L36-Resource Constrained WSD; Parsing

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    L35-Word Sense Disambiguation: Semi - Supervised and Unsupervised method; resource - constrained WSD

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    L34-Word Sense Disambiguation: Supervised and Unsupervised methods

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    L33-Word Sense Disambiguation; Overlap Based Method; Supervised Method

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    L32-Word Sense Disambiguation

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    L31-Wordnet; Metonymy and Word Sense Disambiguation

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