CS388:NaturalLanguageProcessing

GregDurre8

Lecture25:Mul

Administrivia

‣ Project2backtoday/tomorrow

‣ TACCalloca

Dealingwithotherlanguages

‣ManyalgorithmssofarhavebeendevelopedforEnglish‣ Somestructureslikecons

ThisLecture

‣Morphologicalrichness:effectsandchallenges

‣ Cross-lingualtaggingandparsing

‣Morphologytasks:analysis,inflec

Morphology

Whatismorphology?‣ Studyofhowwordsform

‣ Derivaestrangement(n)

become(v)=>unbecoming(adj)

Ibecome/shebecomes

‣ Inflecinflammable

‣Mostlyappliestoverbsandnouns

MorphologicalInflec

MorphologicalInflec

NounInflec

IrregularInflec

Agglu

Morphologically-RichLanguages

‣ManylanguagesusedallovertheworldhavemuchrichermorphologythanEnglish(Chineseisthemainexcep

Morphologically-RichLanguages

‣ Greatresourcesforchallengingyourassump

MorphologicalAnalysis/Inflec

MorphologicalAnalysis:Hungarian

Ámakormányegyetlenadócsökkentésétsemjavasolja.

n=singular|case=nomina/ve|proper=no

deg=posi/ve|n=singular|case=nomina/ve

n=singular|case=nomina/ve|proper=no

n=singular|case=accusa/ve|proper=no|pperson=3rd|pnumber=singular

mood=indica/ve|t=present|p=3rd|n=singular|def=yes

Butthegovernmentdoesnotrecommendreducingtaxes.

MorphologicalAnalysis

‣ Givenaword,needtopredictwhatitsmorphologicalfeaturesare

‣ LotsofworkonArabicinflec

Predic

Predic

MorphologicalReinflec

WordSegmenta

MorphemeSegmentaun+becom+ing—weshouldbeabletorecognizethesecommonpiecesandsplitthemoff

‣ Howdowedothis?

MorphemeSegmenta

ChineseWordSegmenta

Cross-LingualTaggingandParsing

Cross-LingualTagging

‣ LabelingPOSdatasetsisexpensive‣ Canwetransferannota

UnsupervisedTagging

‣Mul

TaggingbyAnnota

Cross-LingualTagging

DasandPetrov(2011)

‣ EM-HMM/featureHMM:unsupervisedmethodswithagreedymappingfromlearnedtagstogoldtags

‣ Projec

Cross-LingualParsing

McDonaldetal.(2011)

‣ NowthatwecanPOStagotherlanguages,canweparsethemtoo?

‣ Directtransfer:trainaparseroverPOSsequencesinonelanguage,thenapplyittoanotherlanguage

Iliketomatoes

PRONVERBNOUN

JelesaimePRONPRONVERB

Ilikethem

PRONVERBPRON

Parsertrained 
toaccepttag
input

VERBistheheadofPRONandNOUN

parsenew 
data

train

Cross-LingualParsing

McDonaldetal.(2011)

‣Mul

Cross-LingualWordRepresenta

Mul

Mul

Mul

Mul

Mul

MulHindi(Devanagari).Transferswelldespitedifferentalphabets!

‣ Japanese=>English:differentscriptandverydifferentsyntax

Mul

Wherearewenow?

‣ Universaldependencies:treebanks(+tags)for70+languages

‣Manylanguagesares

Takeaways

‣ManylanguageshaverichermorphologythanEnglishandposedis