Natural Language Processing

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3 STUDENTS

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

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).

Course Curriculum

Introduction Details 53:19
Stages of NLP Details 52:55
Stages of NLP Continue… Details 58:6
Two approaches to NLP Details 49:31
Sequence Labelling and Noisy Channel Details 48:32
Noisy Channel: Argmax Based Computation Details 49:12
Argmax Based Computation Details 47:7
Noisy Channel Application to NLP Details 50:34
Brief on Probabilistic Parsing & Start of Part of Speech Tagging Details 48:20
Part of Speech Tagging Details 48:26
Part of Speech Tagging counted… Details 47:23
Part of Speech Tagging counted… & Indian Language in Focus; Morphology Analysis Details 45:5
PoS Tagging contd… , Indian Language Consideration; Accuracy Measure Details 45:7
PoS Tagging; Fundamental Principle; Why Challenging; accuracy Details 45:5
PoS Tagging; Accuracy Measurement; Word categories Details 47:40
AI and Probability; HMM Details 48:4
HMM Details 0:46
HMM, Viterbi, Forward Backward Algorithm I Details 44:59
HMM, Viterbi, Forward Backward Algorithm Contd..9 II Details 44:28
HMM, Forward Backward Algorithms, Baum Welch Algorithm III Details 41:41
HMM, Forward Backward Algorithms, Baum Welch Algorithm Contd… IV Details 47:44
Natural Language Processing and Informational Retrieval Details 46:47
CLIA; IR Basics Details 49:25
IR Models: Boolean Vector Details 48:16
IR Models: NLP and IR Relationship I Details 46:26
NLP and IR: How NLP has used IR, Toward Latent Semantic Details 47:46
Least Square Method; Recap of PCA; Towards Latent Semantic Indexing(LSI) Details 41:51
PCA; SVD; Towards Latent Semantic Indexing(LSI) Details 38:54
Wordnet and Word Sense Disambiguation Details 46:40
Wordnet and Word Sense Disambiguation(contd…) I Details 47:23
Wordnet; Metonymy and Word Sense Disambiguation II Details 49:16
Word Sense Disambiguation Details 49:7
Word Sense Disambiguation; Overlap Based Method; Supervised Method III Details 50:7
Word Sense Disambiguation: Supervised and Unsupervised methods IV Details 43:10
Word Sense Disambiguation: Semi – Supervised and Unsupervised method V Details 46:58
Resource Constrained WSD; Parsing Details 48:21
Parsing Details 46:29
Parsing Algorithm Details 47:48
Parsing Ambiguous Sentences; Probabilistic Parsing Details 49:49
Probabilistic Parsing Algorithms Details 47:36

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