Natural Language Processing

Overview Contents: Sound : Biology of Speech Processing; Place and Manner of Articulation; Word Boundary Detection; Argmax based computations; HMM and Speech Recognition.

Beginner 0(0 Ratings) 0 Students enrolled English
Created by skill expert
Last updated Mon, 20-Jun-2022
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Course overview

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).

Curriculum for this course
40 Lessons 31:41:12 Hours
Lecture
40 Lessons 31:41:12 Hours
  • Introduction
    Preview 00:53:19
  • Stages of NLP
    00:52:55
  • Stages of NLP 1
    00:58:06
  • Two approaches to NLP
    00:49:30
  • Sequence Labelling and Noisy Channel
    00:48:31
  • Noisy Channel Argmax Based Computation
    00:49:12
  • Argmax Based Computation
    00:47:07
  • Noisy Channel Application to NLP
    00:50:33
  • Brief on Probabilistic Parsing & Start of Part of Speech Tagging
    00:48:20
  • Part of Speech Tagging
    00:48:26
  • Part of Speech Tagging 1
    00:47:22
  • PoS& Indian Language in Focus; Morphology Analysis
    00:45:05
  • PoS Tagging 3 , Indian Language Consideration; Accuracy Measure
    00:45:07
  • PoS Tagging 4; Fundamental Principle; Why Challenging; accuracy
    00:45:04
  • PoS Tagging 5; Accuracy Measurement; Word categories
    00:47:39
  • AI and Probability; HMM
    00:48:03
  • HMM
    00:45:59
  • HMM, Viterbi, Forward Backward Algorithm I
    00:44:59
  • HMM, Viterbi, Forward Backward Algorithm II
    00:44:28
  • HMM, Forward Backward Algorithms, Baum Welch Algorithm III
    00:41:41
  • HMM, Forward Backward Algorithms, Baum Welch Algorithm IV
    00:47:43
  • Natural Language Processing and Informational Retrieval
    00:46:47
  • CLIA; IR Basics
    00:49:25
  • IR Models Boolean Vector
    00:48:15
  • IR Models NLP and IR Relationship I
    00:46:25
  • NLP and IR How NLP has used IR, Toward Latent Semantic
    00:47:46
  • Least Square Method; Recap of PCA; Latent Semantic Indexing(LSI)
    00:41:50
  • PCA; SVD; Towards Latent Semantic Indexing(LSI)
    00:38:54
  • Wordnet and Word Sense Disambiguation
    00:46:39
  • Wordnet and Word Sense Disambiguation I
    00:47:23
  • Wordnet; Metonymy and Word Sense Disambiguation II
    00:49:16
  • Word Sense Disambiguation
    00:49:06
  • Overlap Based Method; Supervised Method III
    00:50:07
  • Supervised and Unsupervised methods IV
    00:43:10
  • Semi - Supervised and Unsupervised method V
    00:46:58
  • Resource Constrained WSD; Parsing
    00:48:21
  • Parsing
    00:46:29
  • Parsing Algorithm
    00:47:48
  • Parsing Ambiguous Sentences; Probabilistic Parsing
    00:49:48
  • Probabilistic Parsing Algorithms
    00:47:36
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