Lecturer –John McKenna – –Room L2.47 –Phone (700)5507 Tutor –Mairéad McCarthy – CA261 Computational Linguistics II

Module Aims This course introduces the major concepts and techniques in the area of Computational Linguistics, with particular emphasis on probabilistic approaches. Developing and harnessing knowledge to date in order to produce implementations of theoretical concepts.

Admin Lectures –Tuesday 10-11am L2.40 –Friday 11am-12noon L2.17 Lab –Thursday 3-5pm LG.27 –Start Week 1! (This Thursday) Assessment –Exam 50 % –Cont. Ass. 50%

Continuous Assessment A variety of assignments (may overlap) –Mostly programming based Java XFST Tools Weighted by weeks needed for completion –1 week = 5% –Extra week as slack  no extensions You choose how to make up 50% E.g. Assignment issued Week 4 –Worth 15%  3 weeks + 1 week slack  due Week 8

What you need Some knowledge of morphology, phonology, syntax and FSAs –CA162, CA260 Some knowledge of elementary probability –MS142 Ability to program in Java (or Perl) –CA213

What to expect Not a distance education course! Do the readings –Jurafsky, D. & Martin, J. H., Speech and Language Processing, Prentice Hall, 2000 Don’t miss labs without good reason –Priority given to regulars No spoon-feeding –Be resourceful! Flag us if you feel you’re struggling

Module Aims This course introduces the major concepts and techniques in the area of Computational Linguistics, with particular emphasis on probabilistic approaches. Developing and harnessing knowledge to date in order to produce implementations of theoretical concepts.

Other Learning Outcomes You will develop: –Communication skills –Group Work skills –Organisational skills –Personal skills –Problem solving skills –Programming skills –Information Technology skills

Syllabus Finite State Automata –deterministic and non-deterministic Finite State Transducers Computational Morphology Computational Phonology Spelling Correction Weighted Automata N-grams and smoothed N-grams Part-of-speech tagging Probabilistic CFGs