Nptel: Natural Language Processing - Video Course

NPTEL Syllabus
Natural Language Processing - Video

course
COURSE OUTLINE
NPTEL
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

http://nptel.iitm.ac.in
o f Indian Languages; Morphology Paradigms; Finite State Machine Based
Morphology; Automatic Morphology Learning; Shallow Parsing; Named Entities;
Maximum Entropy Models; Random Fields. Computer Science
Structures : Theories of Parsing, Parsing Algorithms; Robust and Scalable and Engineering
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

Pre-requisites:
Wo rd n e ts and Multilingual Dictionaries; Semantic Roles; Word Sense
Disambiguation; WSD and Multilinguality; Metaphors; Coreferences.
1. A previous course on Artificial
Web 2.0 Applications : Sentiment Analysis; Text Entailment; Robust and Intelligence will help.
Scalable Machine Translation; Question Answering in Multilingual Setting;
Cross Lingual Information Retrieval (CLIR). 2. Courses of Data Structures and
Algorithms should have been
COURSE DETAIL done.
A video course shall consist of 40 or more lectures with 1 hour duration per 3. Exposure to Linguistics is useful,
lecture. though not mandatory.
Additional Reading:
Lecture No. Topics
1. Radford, Andrew et. al.,
Linguistics, An Introduction,
1 Introduction Cambridge University Press,
1999.
2. Journals : Computational
2 Machine Learning and NLP Linguistics, Natural Language
Engineering, Machine Learning,
Machine Translation, Artificial
3 ArgMax Computation Intelligence.
3. Conferences : Annual Meeting of

the Association of Computational
4 WSD : WordNet Linguistics (ACL), Computational
Linguistics (COLING), European
ACL (EACL), Empirical Methods
5 Wordnet; Application in Query Expansion in NLP (EMNLP), Annual
Meeting of the Special Interest
Group in Information Retrieval
(SIGIR), Human Language
6 Wiktionary; semantic relatedness Technology (HLT).
Hyperlinks:
7 Measures of WordNet Similarity
http://www.cse.iitb.ac.in/~cs626-449
8 Similarity Measures (contd.)

Coordinators:
Prof. Pushpak Bhattacharyya
9 Resnick's work on WordNet Similarity Department of Computer Science and
EngineeringIIT Bombay
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
References:
1. Allen, James, Natural Language Understanding, Second Edition,

Benjamin/Cumming, 1995.
2. Charniack, Eugene, Statistical Language Learning, MIT Press, 1993.
3. Jurafsky, Dan and Martin, James, Speech and Language Processing,

Second Edition, Prentice Hall, 2008.
4. Manning, Christopher and Heinrich, Schutze, Foundations of Statistical

Natural Language Processing, MIT Press, 1999.
A joint venture by IISc and IITs, funded by MHRD, Govt of India http://nptel.iitm.ac.in

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NPTEL Syllabus

Natural Language Processing - Video

Words and Word Forms : Morphology fundamentals; Morphological Diversity

Meaning : Lexical Knowledge Networks, Wordnet Theory; Indian Language

3. Conferences : Annual Meeting of

8 Similarity Measures (contd.)

11 Evidence for Deeper Structure; Top Down Parsing

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

22 Graphical Models for Sequence Labelling in NLP

23 Graphical Models for Sequence Labelling in NLP (contd.)

25 Consonants (place and manner of articulation) and Vowels

27 Forward Backward probability; Viterbi Algorithm

29 Sentiment Analysis and Opinions on the Web

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

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

1. Allen, James, Natural Language Understanding, Second Edition,

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

3. Jurafsky, Dan and Martin, James, Speech and Language Processing,

4. Manning, Christopher and Heinrich, Schutze, Foundations of Statistical

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