NPTEL IIT Natural Language Processing [40 flv]

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NPTEL - IIT - Natural Language Processing [40 flv]
English | Size: 4.10 GB
Category: Tutorial

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.

NPTEL - IIT - Natural Language Processing [40 flv]
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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 DETAIL

A video course shall consist of 40 or more lectures with 1 hour duration per lecture.

Lecture No.

Topics

1

Introduction

2

Machine Learning and NLP

3

ArgMax Computation

4

WSD : WordNet

5

Wordnet; Application in Query Expansion

6

Wiktionary; semantic relatedness

7

Measures of WordNet Similarity

8

Similarity Measures (contd.)

9

Resnick's work on WordNet Similarity

10

Parsing Algorithms

11

Evidence for Deeper Structure; Top Down Parsing Algorithms

12

Noun Structure; Top Down Parsing Algorithms- contd

13

Non-noun Structure and Parsing Algorithms

14

Probabilistic parsing; sequence labeling, PCFG

15

Probabilistic parsing; PCFG (contd.)

16

Probabilistic parsing: Training issues

17

Arguments and Adjuncts

18

Probabilistic parsing; inside-outside probabilities

19

Speech : Phonetics

20

HMM

21

Morphology

22

Graphical Models for Sequence Labelling in NLP

23

Graphical Models for Sequence Labelling in NLP (contd.)

24

Phonetics

25

Consonants (place and manner of articulation) and Vowels

26

Vowels (contd.)

27

Forward Backward probability; Viterbi Algorithm

28

Phonology

29

Sentiment Analysis and Opinions on the Web

30

Machine Translation and MT Tools - GIZA++ and Moses.

31

Text Entailment

32

POS Tagging.

33

Phonology; ASR, Speech Synthesis

34

HMM and Viterbi

35

HMM and Viterbi (contd)

36

Precision, Recall, F-score, Map

37

Semantic Relations; UNL; Towards Dependency Parsing.

38

Universal Networking Language

39

Semantic Role Extraction

40

Baum Welch Algorithm; HMM training

41

Baum Welch Algorithm; HMM training

PREREQUISITES

A previous course on Artificial Intelligence will help.

Courses of Data Structures and Algorithms should have been done.

Exposure to Linguistics is useful, though not mandatory.

REFERENCES

Allen, James, Natural Language Understanding, Second Edition, Benjamin/Cumming, 1995.

Charniack, Eugene, Statistical Language Learning, MIT Press, 1993.

Jurafsky, Dan and Martin, James, Speech and Language Processing, Second Edition, Prentice Hall, 2008.

Manning, Christopher and Heinrich, Schutze, Foundations of Statistical Natural Language Processing, MIT Press, 1999.

RELATED LINKS

http://www.cse.iitb.ac.in/~cs626-449

ADDITIONAL READINGS

Radford, Andrew et. al., Linguistics, An Introduction, Cambridge University Press, 1999.

Journals : Computational Linguistics, Natural Language Engineering, Machine Learning, Machine Translation, Artificial Intelligence.

Conferences : Annual Meeting of the Association of Computational Linguistics (ACL), Computational Linguistics (COLING), European ACL (EACL), Empirical Methods in NLP (EMNLP), Annual Meeting of the Special Interest Group in Information Retrieval (SIGIR), Human Language Technology (HLT).
NPTEL - IIT - Natural Language Processing [40 flv]
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