MAS5006_FUNDAMENTALS-OF-COMPUTATIONAL-LINGUISTICS_LT_1.0_35_Fundamentals of Computational Linguistics (1).pdf
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This document outlines a course on fundamentals of computational linguistics. The course introduces students to linguistics and natural language processing. It aims to help students understand the theoretical components of language and complexities of natural languages. Over the course, students will learn about phonetics, morphology, syntax, semantics and their application in natural language processing. They will study theories of language and complete activities applying linguistic concepts to speech analysis and natural language tasks. The course will be taught through lectures, activities and guest lectures to provide both theoretical and practical knowledge in the field.
![Fundamentals of Computational Linguistics
Course Type LT
Course Code: MAS5006 Credits 3
Prerequisite: Concept of natural language and programming knowledge
Course Objectives:
1. To introduce students to the fundamentals of linguistics and understanding of natural language processing
2. The course will serve the prerequisite to follow the deeper and complex linguistic processes of natural
languages with special emphasis to the processes under the Natural Language Processing tools.
Course Outcomes (CO):
At the end of the course, students should be able to
CO1. The students will be able to appreciate the theoretical components of language sciences and underlying
complexities of natural languages.
CO2. The students will be able to comprehend and conceptualize the concepts of Phonetics and its efficacy
in the field of Natural Language Processing.
CO3. The students will be able to understand and apply the theoretical and application-based processes of
morphological analyses in relation to the NLP processes.
CO4. The students will be able to comprehend the syntactic role and function of Natural languages.
CO5. The students will be able to process the semantic and pragmatic challenges under the NLP processes.
Correlation of COs with POs
CO PO CKL PO1 PO2 PO3 PSO1 PSO2
PKL 3 5 6 3 3
CO1 2 3 3 2 2 2
CO2 2 2 3 2 2 2
CO3 3 3 3 3 3 3
CO4 3 2 2 3 3 3
CO5 4 3 3 3 3 3
CO Topics to be discussed Hrs.
CO1 Fundamental Concept of Language Sciences (Linguistics)
a. Design Features of Languages
b. Language Acquisition and Learning Process
c. Language and Linguistics theories:
[i. Saussure’s Key Theories of Language, ii. Skinner’s theory of language
development “Behaviourism”,
iii. Chomsky’s theory of Universal Grammar and Principals & Parameters]
d. Role of language in Computational Linguistics and Cognitive Science
e. Fundamental concepts of Natural Language Processing
Activity: Perception and Knowledge of Mother tongue vs Second language learning
experiences using the language learning theories.
06
CO2 Elementary Phonetics and its Implication in Natural Language Processing
a. Organs of Speech,
b. Description, classification of speech sounds (Consonantal and Vowel sounds) and
their places and manners of articulation
c. Implication, role and application of IPA, The concept of Phone, Phoneme and
Allophone, Elementary phonological knowledge and types of phonological
processes [distinctive features, minimal pairs, language specific contrasts,
complementary distribution and contrastive distribution of speech sounds]
08](https://image.slidesharecdn.com/mas5006fundamentals-of-computational-linguisticslt1-240110100917-7c64f3fc/85/MAS5006_FUNDAMENTALS-OF-COMPUTATIONAL-LINGUISTICS_LT_1-0_35_Fundamentals-of-Computational-Linguistics-1-pdf-1-320.jpg)
![d. An Introduction to the process of Automatic Speech Recognition
Activity: Phonetic transcription, Speech segmentation and synthesis of natural languages
through speech samples using PRAAT, ASR modals and analyses of Word Error Rate
under NLP processes.
CO3 Morphology and its Implication in Natural Language Processing
a. The concept of Morph, Morpheme and Allomorph
b. Bound and Free Morphemes, Root and Stems
c. Rules of Word, Phrase, Clause and Sentence Structures
d. Rules of Word Formation Processes
[Derivation, Inflection, Conversion, Compounding, Coinage, Borrowing,
Blending, Clipping, Back Formation, Reduplication]
e. POS Identification and tagging using UPenn Tagset
f. Application of Parsing in NLP
g. Application of Chunking in NLP
h. Application of Word Sense Disambiguation in NLP
Activity: POS Tagging, Application and Processing of Morphological Analyser
08
CO4 Application of Syntax in Natural Language Processing
a. Syntactic Rules
b. Constituents and Constituency Tests
c. Phrase Structure Rules
d. Structural Ambiguities
e. Context Free Grammar,
f. Tree Structures Rules
g. Transformations [Wh-movement, yes and no-inversion]
Activity: Tree Structures of simple and complex sentences
08
CO5 Application of Semantics in Natural Language Understanding
a. Truth and Entailment relationships
b. Semantic ambiguities
c. Compositional Semantics
d. Lexical Semantics
e. Introduction to Pragmatics
f. Speech Acts
g. Maxim of Conversations
h. An Introduction to sentiment analyses
Activity: Speech act analyses, Plan recognition.
08
Guest Lecture on Contemporary Topics 02
Suggested Cloud Automation Tools: AWS CloudFormation, Terraform, VMware v
Center Configuration Manager (VCM), and Puppet
Total Lecture: 40
Mode of Teaching and Learning:
Flipped Class Room, Activity Based Teaching/Learning, Digital/Computer based models, wherever possible
to augment lecture for practice/tutorial and minimum 2 hours lecture by industry experts on contemporary
topics.
Mode of Evaluation and assessment:
The assessment and evaluation components may consist of unannounced open book examinations, quizzes,
student’s portfolio generation and assessment, and any other innovative assessment practices followed by
faculty, in addition to the Continuous Assessment Tests and Term End Examination.](https://image.slidesharecdn.com/mas5006fundamentals-of-computational-linguisticslt1-240110100917-7c64f3fc/85/MAS5006_FUNDAMENTALS-OF-COMPUTATIONAL-LINGUISTICS_LT_1-0_35_Fundamentals-of-Computational-Linguistics-1-pdf-2-320.jpg)
MAS5006_FUNDAMENTALS-OF-COMPUTATIONAL-LINGUISTICS_LT_1.0_35_Fundamentals of Computational Linguistics (1).pdf
- 1. Fundamentals of Computational Linguistics Course Type LT Course Code: MAS5006 Credits 3 Prerequisite: Concept of natural language and programming knowledge Course Objectives: 1. To introduce students to the fundamentals of linguistics and understanding of natural language processing 2. The course will serve the prerequisite to follow the deeper and complex linguistic processes of natural languages with special emphasis to the processes under the Natural Language Processing tools. Course Outcomes (CO): At the end of the course, students should be able to CO1. The students will be able to appreciate the theoretical components of language sciences and underlying complexities of natural languages. CO2. The students will be able to comprehend and conceptualize the concepts of Phonetics and its efficacy in the field of Natural Language Processing. CO3. The students will be able to understand and apply the theoretical and application-based processes of morphological analyses in relation to the NLP processes. CO4. The students will be able to comprehend the syntactic role and function of Natural languages. CO5. The students will be able to process the semantic and pragmatic challenges under the NLP processes. Correlation of COs with POs CO PO CKL PO1 PO2 PO3 PSO1 PSO2 PKL 3 5 6 3 3 CO1 2 3 3 2 2 2 CO2 2 2 3 2 2 2 CO3 3 3 3 3 3 3 CO4 3 2 2 3 3 3 CO5 4 3 3 3 3 3 CO Topics to be discussed Hrs. CO1 Fundamental Concept of Language Sciences (Linguistics) a. Design Features of Languages b. Language Acquisition and Learning Process c. Language and Linguistics theories: [i. Saussure’s Key Theories of Language, ii. Skinner’s theory of language development “Behaviourism”, iii. Chomsky’s theory of Universal Grammar and Principals & Parameters] d. Role of language in Computational Linguistics and Cognitive Science e. Fundamental concepts of Natural Language Processing Activity: Perception and Knowledge of Mother tongue vs Second language learning experiences using the language learning theories. 06 CO2 Elementary Phonetics and its Implication in Natural Language Processing a. Organs of Speech, b. Description, classification of speech sounds (Consonantal and Vowel sounds) and their places and manners of articulation c. Implication, role and application of IPA, The concept of Phone, Phoneme and Allophone, Elementary phonological knowledge and types of phonological processes [distinctive features, minimal pairs, language specific contrasts, complementary distribution and contrastive distribution of speech sounds] 08
- 2. d. An Introduction to the process of Automatic Speech Recognition Activity: Phonetic transcription, Speech segmentation and synthesis of natural languages through speech samples using PRAAT, ASR modals and analyses of Word Error Rate under NLP processes. CO3 Morphology and its Implication in Natural Language Processing a. The concept of Morph, Morpheme and Allomorph b. Bound and Free Morphemes, Root and Stems c. Rules of Word, Phrase, Clause and Sentence Structures d. Rules of Word Formation Processes [Derivation, Inflection, Conversion, Compounding, Coinage, Borrowing, Blending, Clipping, Back Formation, Reduplication] e. POS Identification and tagging using UPenn Tagset f. Application of Parsing in NLP g. Application of Chunking in NLP h. Application of Word Sense Disambiguation in NLP Activity: POS Tagging, Application and Processing of Morphological Analyser 08 CO4 Application of Syntax in Natural Language Processing a. Syntactic Rules b. Constituents and Constituency Tests c. Phrase Structure Rules d. Structural Ambiguities e. Context Free Grammar, f. Tree Structures Rules g. Transformations [Wh-movement, yes and no-inversion] Activity: Tree Structures of simple and complex sentences 08 CO5 Application of Semantics in Natural Language Understanding a. Truth and Entailment relationships b. Semantic ambiguities c. Compositional Semantics d. Lexical Semantics e. Introduction to Pragmatics f. Speech Acts g. Maxim of Conversations h. An Introduction to sentiment analyses Activity: Speech act analyses, Plan recognition. 08 Guest Lecture on Contemporary Topics 02 Suggested Cloud Automation Tools: AWS CloudFormation, Terraform, VMware v Center Configuration Manager (VCM), and Puppet Total Lecture: 40 Mode of Teaching and Learning: Flipped Class Room, Activity Based Teaching/Learning, Digital/Computer based models, wherever possible to augment lecture for practice/tutorial and minimum 2 hours lecture by industry experts on contemporary topics. Mode of Evaluation and assessment: The assessment and evaluation components may consist of unannounced open book examinations, quizzes, student’s portfolio generation and assessment, and any other innovative assessment practices followed by faculty, in addition to the Continuous Assessment Tests and Term End Examination.
- 3. Text Book(s): 1. The Oxford Handbook of Computational Linguistics (Oxford Handbooks), Ruslan Mitkov, OUP Oxford; 2nd edition (June 2, 2022), 2nd Edition. 2. Handbook of Computational Linguistics and Natural Language Processing, Martin Whitehead, Clanrye International (September 15, 2020). 3. Computational Linguistics: An Introduction, Ralph Grishhman, Cambridge University Press, 1986. 2. Introduction to Natural Language Processing, Jacob Eisenstein, MIT Press, 2019 4. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguitsics and Speech Recognition, Daniel Jurafsky, James H Martin, Pearson, 2013 rev.ed 5. Andrew Radford, Martin Atkinson, David Britin, Harald Clahsen, Andrew Spencer, (2003), Introduction to Linguistics, Cambridge University Press, New York, USA. 6. Stuart C. Poole, (1999), An Introduction to Linguistics, Palgrave Macmillan British Library Catalogue, Hoboken USA. Reference Books: 1. Daniel Jurafsky and James H. Martin, Speech and Language Processing, Prentice- Hall, 2000. 2. Patrick Blackburn and Kristina Striegnitz (BS) Natural Language Processing Techniques in Prolog. 3. Patrick Blackburn and Johan Bos (BB1) Representation and Inference for Natural Language A First Course in Computational Semantics. 4. Patrick Blackburn and Johan Bos (BB2) Working with Discourse Representation Theory. 5. Mohamed Zakaria Kurdi, Natural Language Processing and Computational Linguistics-1, British. 6. Ralph Grishman (1986), Computational Linguistics: An Introduction, Cambridge University Press, USA 7. Daniel Jurafsky and James H. Martin, (1999), Speech and Language Processing:An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, Alan Apt, New Jersey 8. Patrick Blackburn, Kristina Striegnitz, (2009), Natural Language Processing Techniques in Prolog, O'Reilly Media; 1 edition. 9. Patrick Blackburn, Johan Bos (2005)Representation and Inference for Natural Language: A First Course in Computational Semantics (Studies in Computational Linguistics), Center for the Study of Language and Information, 10. Jackendoff, Ray, (2002), Foundations of language: brain, meaning, grammar, evolution, Oxford University Press, UK. Recommendation by the Board of Studies on 9.1.2023 Approval by Academic council on: 26.5.2023 Compiled by: Dr. Pradeep Kumar Mishra