Natural Language Processing (NLP) is semester 8 subject of final year of computer engineering in Mumbai University. Prerequisite for studying this subject are Data structure & Algorithms, Theory of computer science, Probability Theory.
Module Introduction to Natural Language Processing covered topics are as following History of Natural Language Processing, Generic Natural Language Processing system, levels of Natural Language Processing, Knowledge in language processing, Ambiguity in Natural language stages in Natural Language Processing, challenges of Natural Language Processing ,Applications of Natural Language Processing. Module Word Level Analysis covers topics such as Morphology analysis –survey of English Morphology, Inflectional morphology & Derivational morphology, Lemmatization, Regular expression, finite automata, finite state transducers (FST) ,Morphological parsing with FST , Lexicon free FST Porter stemmer. N –Grams- N-gram language model, N-gram for spelling correction. Module Syntax analysis covers topics such as Part-Of-Speech tagging( POS)- Tag set for English ( Penn Treebank ) , Rule based POS tagging, Stochastic POS tagging, Issues –Multiple tags & words, Unknown words. Introduction to CFG, Sequence labeling: Hidden Markov Model (HMM), Maximum Entropy, and Conditional Random Field (CRF). Module Semantic Analysis covers topics such as Lexical Semantics, Attachment for fragment of English- sentences, noun phrases, Verb phrases, prepositional phrases, Relations among lexemes & their senses –Homonymy, Polysemy, Synonymy, Hyponymy, WordNet, Robust Word Sense Disambiguation (WSD), Dictionary based approach. Module Pragmatics covers topics such as Discourse –reference resolution, reference phenomenon, syntactic & semantic constraints on co reference. Module Applications (preferably for Indian regional languages) covers topics such as Machine translation, Information retrieval, Question answers system, categorization, summarization, sentiment analysis, Named Entity Recognition.
Course Objectives of the subject Natural language processing To understand natural language processing and to learn how to apply basic algorithms in this field. To get acquainted with the basic concepts and algorithmic description of the main language levels morphology, syntax, semantics, and pragmatics. To design and implement applications based on natural language processing to implement various Natural language Processing Models. To design systems that uses Natural language processing techniques. On successful completion of natural language processing course learner will have a broad understanding of the field of natural language processing. With that learner gains a sense of the capabilities and limitations of current natural language technologies, learner will be able to model linguistic phenomena with formal grammars. Learner will be able to Design, implement and test algorithms for natural language processing problems Understand the mathematical and linguistic foundations underlying approaches to the various areas in natural language processing be able to apply natural language processing techniques to design real world NLP applications such as machine translation, text categorization, text summarization, information extraction…etc. Suggested Texts Books for Natural Language Processing by Mumbai University are as follows Daniel Jurafsky, James H. Martin ―Speech and Language Processing‖ Second Edition, Prentice Hall, 2008.Christopher D.Manning and Hinrich Schutze, ― Foundations of Statistical Natural Language Processing ―, MIT Press, 1999.Suggested Reference Books for Natural Language Processing by Mumbai University are as follows Siddiqui and Tiwary U.S., Natural Language Processing and Information Retrieval, Oxford University Press (2008).Daniel M Bikel and Imed Zitouni ― Multilingual natural language processing applications‖ Pearson.2013. Alexander Clark (Editor), Chris Fox (Editor), Shalom Lappin (Editor) ― The Handbook of Computational Linguistics and Natural Language Processing ― ISBN: 978-1-118. Steven Bird, Ewan Klein, Natural Language Processing with Python, O‘Reilly. Brian Neil Levine, An Introduction to R Programming. Niel J le Roux, Sugnet Lubbe, A step by step tutorial : An introduction into R application and Programming.
Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. The result is a computer capable of “understanding” the contents of documents, including the contextual nuances of the language within them. The technology can then accurately extract information and insights contained in the documents as well as categorize and organize the documents themselves. Challenges in natural language processing frequently involve speech recognition, natural language understanding, and natural-language generation.
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Course Features
- Lectures 39
- Quiz 0
- Duration 50 hours
- Skill level All levels
- Language English
- Students 67
- Assessments Yes
Curriculum
- 3 Sections
- 39 Lessons
- 43 Weeks
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0 - Index31
- 2.1Introduction to NLP [Natural Language Processing] (Module 1)12 Minutes
- 2.2Knowledge Required in NLP (Module 1)11 Minutes
- 2.3Ambiguity in NLP (Module 1)7 Minutes
- 2.4NLP Phases (Module 1)8 Minutes
- 2.5Morphology Analysis (Module 2)11 Minutes
- 2.6Regular Expression (Module 2)9 Minutes
- 2.7FSA (Module 2)9 Minutes
- 2.8Morphology Parsing (Module 2)9 Minutes
- 2.9Language Model (Module 2)10 Minutes
- 2.10N-gram Model (Module 2)4 Minutes
- 2.11Syntax Analysis (Module 3)3 Minutes
- 2.12POS Tagging (Module 3)10 Minutes
- 2.13Tag-set for English (Module 3)12 Minutes
- 2.14Rule Based POS (Module 3)6 Minutes
- 2.15Stochastic Part of Speech Tagging (Module 3)8 Minutes
- 2.16Transformation Based Tagging (Module 3)6 Minutes
- 2.17Multiple Tags ,Word and Unknown Words (Module 3)4 Minutes
- 2.18Basic Concept of Grammar and Parse Tree (Module 3)9 Minutes
- 2.19Parsing in NLP (Module 3)6 Minutes
- 2.20Hidden Markov Model Part 1 (Module 3)10 Minutes
- 2.21Hidden Markov Model Part 2 (Module 3)7 Minutes
- 2.22Viterbi Algorithm (Module 3)8 Minutes
- 2.23Introduction to Semantic Analysis (Module 4)13 Minutes
- 2.24Element of Semantic Analysis ( Module 4)6 Minutes
- 2.25Attachment for Fragment of English (Phrases #1) (Module 4)9 Minutes
- 2.26Attachment for Fragment of English (Phrases #2) (Module 4)5 Minutes
- 2.27Attachment for Fragment of English (Phrases #3) (Module 4)4 Minutes
- 2.28WordNet (Module 4)8 Minutes
- 2.29Word Sense Disambiguation (WSD)(Module 4)9 Minutes
- 2.30Pragmatics13 Minutes
- 2.31Discourse Processing16 Minutes
- Notes + MCQs + Viva Questions8
