THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2014/2015
- ARCHIVE as at 1 September 2014

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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Informatics 2A - Processing Formal and Natural Languages (INFR08008)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 8 (Year 2 Undergraduate) Credits20
Home subject areaInformatics Other subject areaNone
Course website http://course.inf.ed.ac.uk/inf2a Taught in Gaelic?No
Course descriptionThis course is about processing natural and artificial languages, building on material covered in Informatics 1 concerning finite state machines and regular expressions. This course will consider how the same models of language can be used to describe and analyse both formal languages (such as programming languages) and natural languages (text and speech). It will include material on formal languages and grammars, probabilistic grammars (including hidden Markov models), semantic analysis and human language processing. Examples will be drawn from computer languages and natural language.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Informatics 1 - Computation and Logic (INFR08012) AND Informatics 1 - Data and Analysis (INFR08015) AND Informatics 1 - Functional Programming (INFR08013) AND Informatics 1 - Object-Oriented Programming (INFR08014)
Co-requisites
Prohibited Combinations Other requirements None
Additional Costs None
Information for Visiting Students
Pre-requisitesNone
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2014/15 Semester 1, Available to all students (SV1) Learn enabled:  Yes Quota:  None
Web Timetable Web Timetable
Course Start Date 15/09/2014
Breakdown of Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 30, Seminar/Tutorial Hours 9, Supervised Practical/Workshop/Studio Hours 18, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 137 )
Additional Notes
Breakdown of Assessment Methods (Further Info) Written Exam 75 %, Coursework 25 %, Practical Exam 0 %
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S1 (December)Informatics 2A - Processing Formal and Natural Languages2:00
Resit Exam Diet (August)2:00
Summary of Intended Learning Outcomes
1 - Demonstrate knowledge of the relationships between languages, grammars and automata, including the Chomsky hierarchy;
2 - Demonstrate understanding of regular languages and finite automata;
3 - Demonstrate understanding of context-free languages and pushdown automata, and how how context-free grammars may be used to model natural language;
4 - Demonstrate knowledge of top-down and bottom-up parsing algorithms for context-free languages;
5 - Demonstrate understanding of probabilistic finite state machines and hidden Markov models, including parameter estimation and decoding;
6 - Demonstrate awareness of probabilistic context-free grammars, and associated parsing algorithms;
7 - Demonstrate knowledge of issues relating to human language processing.
Assessment Information
In order to pass the course you must satisfy all of the following requirements:
* achieve at least 35% in the examination;
* achieve a total of at least 25% in assessed coursework;
* obtain a combined total mark of at least 40%

Assessment
Three pieces of assessed coursework, including computer-based exercises.

You should expect to spend approximately 50 hours on the coursework for this course.
Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus * Grammars and the Chomsky Hierarchy
* Regular languages, Finite state automata (FSA), probabilistic FSAs
* Context-free languages and Push-down automata
* Ambiguity and solutions to the problem
* Deterministic parsers
* Chart parsers
* Probabilistic context-free grammars
* Modelling semantics
* Context-sensitive languages
* Turing machines and computability
* Models of human language processing
* Overview of language technology

Relevant QAA Computing Curriculum Sections: Natural Language Computing; Theoretical Computing; Compilers and Syntax Directed Tools
Transferable skills Not entered
Reading list * Dexter Kozen. Automata and Computability. Springer-Verlag, 2000.
* Dan Jurafsky and James Martin. Speech and Language Processing (*2nd* Edition). Prentice-Hall, 2008.
Natural Language Processing with Python, Bird, Klein & Loper, O'Reilly Publishers 2009
Study Abroad Not entered
Study Pattern Not entered
KeywordsNot entered
Contacts
Course organiserDr John Longley
Tel: (0131 6)50 5140
Email: j.r.longley@ed.ac.uk
Course secretaryMs Kendal Reid
Tel: (0131 6)50 5194
Email: kr@inf.ed.ac.uk
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