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Phonology

Phonology. [These slides are missing most examples and discussion from class …]. Spelling. Pronunciation. cat s dog s rose s kiss es. “kat s ” “dawg z ” “roz iz ” “kis iz ”. why?. phonology doesn’t care about the spelling (that’s just applied morphology). What is Phonology?.

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Phonology

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  1. Phonology [These slides are missing most examples and discussion from class …] 600.465 - Intro to NLP - J. Eisner

  2. Spelling Pronunciation cats dogs roses kisses “kats” “dawgz” “roziz” “kisiz” why? phonology doesn’t care about the spelling (that’s just applied morphology) What is Phonology? • cat + -s • dog + -s • rose + -s • kiss + -s How do you pronounce a sequence of morphemes? Especially, how & why do you fix up the pronunciation at the seams between morphemes? 600.465 - Intro to NLP - J. Eisner

  3. Spelling Pronunciation napped nabbed nodded knotted “næpt” “næbd” “nadid” “natid” Actually, these are pronounced identically: na∂Id thanks to the English “flapping” rule (similarly: ladder/latter, bedding/betting) What is Phonology? • nap + -t • nab + -t • nod + -t • knot + -t 600.465 - Intro to NLP - J. Eisner

  4. What is Phonology? “Trisyllabic Shortening” in English serene  serenity supreme  supremity obscene  obscenity obese  *obesity divine  divinity futile  futility senile  senility satire  satirical decide  decision wild  wilderness (and similarly for other vowels) 600.465 - Intro to NLP - J. Eisner

  5. What is Phonology? • A function twixt head and lip • What class of functions is allowed? • Differs from one language to next • Often complicated, but not arbitrary • Comp Sci: How to compute, invert, learn? Morphology (head) Phonological mapping Articulation (mouth) underlyingphonemes surfacephones resign resign + -ation ree-ZIYN reh-zihg-NAY-shun 600.465 - Intro to NLP - J. Eisner

  6. Rule 1 input (I) Rule 2 output (O) Rule 3 Successive Fixups for Phonology • Chomsky & Halle (1968) • Stepwise refinement of a single form • How to handle “resignation” example? • That is, O = f(I) = g3(g2(g1(I))) • Function composition (e.g., transducer composition) 600.465 - Intro to NLP - J. Eisner

  7. example courtesy of K. Crosswhite How to Give Orders • Directions version: • Break two eggs into a medium mixing bowl. • Remove this tab first. • On the last day of each month, come to this office and pay your rent. • Rules version: • No running in the house is allowed. • All dogs must be on a leash. • Rent must be paid by the first day of each month. • In rules version, describe what a good solution would look like, plus a search procedure for finding the best solution). Where else have we seen this? successive fixup (derivation) successive winnowing (optimization) 600.465 - Intro to NLP - J. Eisner

  8. Optimality Theoryfor Phonology • Prince & Smolensky (1993) • Alternative to successive fixups • Successive winnowing of candidate set . . . Gen Constraint 1 input Constraint 2 Constraint 3 output 600.465 - Intro to NLP - J. Eisner

  9. Optimality Theory “Tableau” «« = candidate violates constraint twice (weight 2) constraint would prefer A, but only allowed to break tie among B,D,E 600.465 - Intro to NLP - J. Eisner

  10. . . . Gen Constraint 1 input (I) Constraint 2 Constraint 3 output (O) Optimality Theoryfor Phonology adds weights to candidates adds weights to candidates best paths (several may tie) best paths (breaks some ties) 600.465 - Intro to NLP - J. Eisner

  11. When do we prune back to best paths? • Optimality Theory: At each intermediate stage • Noisy channel: After adding up all weights . . . output (O) 600.465 - Intro to NLP - J. Eisner

  12. Why does order matter? • Optimality Theory: Each machine (FSA) can choose only among outputs that previous machines liked best • Noisy channel: Each machine (FST) alters the output produced by previous machines . . . output (O) 600.465 - Intro to NLP - J. Eisner

  13. . . . Gen Constraint 1 input (I) Constraint 2 Constraint 3 output (O) Final Remark on OT Repeated best-paths only works for a single input Better to build full FST for I  O (invertible) Can do this e.g. if every constraint is binary: Assigns each candidate either 1 star (“bad”) or 0 stars (“good”) 600.465 - Intro to NLP - J. Eisner

  14. Optimality Theory “Tableau” all surviving candidates violate constraint 3, so we can’t eliminate any 600.465 - Intro to NLP - J. Eisner

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