Undergraduate Course: Informatics 2A - Processing Formal and Natural Languages (INFR08008)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 8 (Year 2 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This 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. |
| Course description |
* 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
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Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate knowledge of the relationships between languages, grammars and automata, including the Chomsky hierarchy
- Demonstrate understanding of regular languages, finite automata, context-free languages and pushdown automata, and how how context-free grammars may be used to model natural language
- Demonstrate knowledge of top-down and bottom-up parsing algorithms for context-free languages
- Demonstrate understanding of probabilistic finite state machines and hidden Markov models, including parameter estimation and decoding, and probabilistic context-free grammars, with associated parsing algorithms
- Demonstrate knowledge of issues relating to human language processing
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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
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Contacts
| Course organiser | Dr Mary Cryan
Tel: (0131 6)50 5153
Email: mcryan@inf.ed.ac.uk |
Course secretary | Ms Kendal Reid
Tel: (0131 6)51 3249
Email: kr@inf.ed.ac.uk |
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