Download presentation

Presentation is loading. Please wait.

Published byDamian Eustace Modified over 2 years ago

1
600.465 - Intro to NLP - J. Eisner1 Phonology [These slides are missing most examples and discussion from class …]

2
600.465 - Intro to NLP - J. Eisner2 “kats” “dawgz” “roziz” “kisiz” Pronunciation What is Phonology? cat + -s dog + -s rose + -s kiss + -s cats dogs roses kisses Spelling How do you pronounce a sequence of morphemes? Especially, how & why do you fix up the pronunciation at the seams between morphemes? why? phonology doesn’t care about the spelling (that’s just applied morphology)

3
600.465 - Intro to NLP - J. Eisner3 “næpt” “næbd” “nadid” “natid” Pronunciation What is Phonology? nap + -t nab + -t nod + -t knot + -t napped nabbed nodded knotted Spelling Actually, these are pronounced identically: na∂ I d thanks to the English “flapping” rule (similarly: ladder/latter, bedding/betting)

4
600.465 - Intro to NLP - J. Eisner4 “Trisyllabic Shortening” in English What is Phonology? divine divinity futile futility senile senility satire satirical decide decision wild wilderness serene serenity supreme supremity obscene obscenity obese *obesity (and similarly for other vowels)

5
600.465 - Intro to NLP - J. Eisner5 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) underlying phonemes surface phones ree-ZIYN reh-zihg-NAY-shun resign resign + -ation

6
600.465 - Intro to NLP - J. Eisner6 Successive Fixups for Phonology Chomsky & Halle (1968) Stepwise refinement of a single form How to handle “resignation” example? That is, O = f( I ) = g 3 (g 2 (g 1 ( I ))) Function composition (e.g., transducer composition) Rule 1 Rule 2 input (I) output (O) Rule 3

7
600.465 - Intro to NLP - J. Eisner7 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) example courtesy of K. Crosswhite

8
600.465 - Intro to NLP - J. Eisner8 Optimality Theory for Phonology Prince & Smolensky (1993) Alternative to successive fixups Successive winnowing of candidate set Gen Constraint 1 Constraint 2 Constraint 3 input... output

9
600.465 - Intro to NLP - J. Eisner9 Optimality Theory “Tableau” constraint would prefer A, but only allowed to break tie among B,D,E = candidate violates constraint twice (weight 2)

10
600.465 - Intro to NLP - J. Eisner10 Optimality Theory for Phonology Gen Constraint 1 Constraint 2 Constraint 3 input (I)... output (O) adds weights to candidates adds weights to candidates best paths (several may tie) best paths (breaks some ties)

11
600.465 - Intro to NLP - J. Eisner11 When do we prune back to best paths? Optimality Theory: At each intermediate stage Noisy channel: After adding up all weights... output (O)

12
600.465 - Intro to NLP - J. Eisner12 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)

13
600.465 - Intro to NLP - J. Eisner13 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”) Gen Constraint 1 Constraint 2 Constraint 3 input (I)... output (O)

14
600.465 - Intro to NLP - J. Eisner14 Optimality Theory “Tableau” all surviving candidates violate constraint 3, so we can’t eliminate any

Similar presentations

OK

600.465 - Intro to NLP - J. Eisner1 Finite-State and the Noisy Channel.

600.465 - Intro to NLP - J. Eisner1 Finite-State and the Noisy Channel.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on condition based maintenance definition Waters view ppt on ipad Ppt on united nations agencies Ppt on multiplexers and demultiplexers Convert doc file to ppt online maker Ppt on cse related topics on personality Ppt on design of earthen dam Ppt on prefilled syringes The solar system for kids ppt on batteries Ppt on different types of government in the world