natural language processing with deep learning...

Natural Language Processingwith Deep Learning

CS224N/Ling284

Richard Socher

Lecture 2: Word Vectors

Organization

• PSet 1isreleased.CodingSession1/22:(Monday,PA1dueThursday)

• SomeofthequestionsfromPiazza:• sharingthechoose-your-ownfinalprojectwithanotherclass

seemsfine-->Yes*• Buthowaboutthedefaultfinalproject?Canthatalsobeused

asafinalprojectforadifferentcourse?-->Yes*• Areweallowingstudentstobringonesheetofnotesforthe

midterm?-->Yes• AzurecomputingresourcesforProjects/PSet4.Partofmilestone

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LecturePlan

1. Wordmeaning(15mins)2. Word2vecintroduction(20mins)3. Word2vecobjectivefunctiongradients(25mins)4. Optimizationrefresher(10mins)

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1.Howdowerepresentthemeaningofaword?

Definition:meaning (Websterdictionary)

• theideathatisrepresentedbyaword,phrase,etc.

• theideathatapersonwantstoexpressbyusingwords,signs,etc.

• theideathatisexpressedinaworkofwriting,art,etc.

Commonestlinguisticwayofthinkingofmeaning:

signifier(symbol)⟺ signified(ideaorthing)

=denotation 1/11/184

Howdowehaveusablemeaninginacomputer?Commonsolution:Usee.g.WordNet,aresourcecontaininglistsofsynonymsets andhypernyms (“isa”relationships).

[Synset('procyonid.n.01'), Synset('carnivore.n.01'), Synset('placental.n.01'), Synset('mammal.n.01'), Synset('vertebrate.n.01'), Synset('chordate.n.01'), Synset('animal.n.01'), Synset('organism.n.01'), Synset('living_thing.n.01'), Synset('whole.n.02'), Synset('object.n.01'), Synset('physical_entity.n.01'), Synset('entity.n.01')]

(adj) full, good (adj) estimable, good, honorable, respectable (adj) beneficial, good (adj) good, just, upright (adj) adept, expert, good, practiced, proficient, skillful(adj) dear, good, near (adj) good, right, ripe…(adv) well, good (adv) thoroughly, soundly, good (n) good, goodness (n) commodity, trade good, good

e.g.synonymsetscontaining“good”: e.g.hypernymsof“panda”:

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ProblemswithresourceslikeWordNet

• Greatasaresourcebutmissingnuance

• e.g.“proficient”islistedasasynonymfor“good”.Thisisonlycorrectinsomecontexts.

• Missingnewmeaningsofwords

• e.g.wicked,badass,nifty,wizard,genius,ninja,bombest

• Impossibletokeepup-to-date!

• Subjective

• Requireshumanlabortocreateandadapt

• Hardtocomputeaccuratewordsimilarityà1/11/186

Representingwordsasdiscretesymbols

IntraditionalNLP,weregardwordsasdiscretesymbols:hotel, conference, motel

Wordscanberepresentedbyone-hot vectors:

motel=[000000000010000]hotel=[000000010000000]

Vectordimension=numberofwordsinvocabulary(e.g.500,000)

Meansone1,therest0s

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Problemwithwordsasdiscretesymbols

Example: inwebsearch,ifusersearchesfor“Seattlemotel”,wewouldliketomatchdocumentscontaining“Seattlehotel”.

But:motel=[000000000010000]hotel=[000000010000000]

Thesetwovectorsareorthogonal.Thereisnonaturalnotionofsimilarityforone-hotvectors!

Solution:• CouldrelyonWordNet’slistofsynonymstogetsimilarity?• Instead:learntoencodesimilarityinthevectorsthemselves

Sec. 9.2.2

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Representingwordsbytheircontext

• Coreidea:Aword’smeaningisgivenbythewordsthatfrequentlyappearclose-by

• “Youshallknowawordbythecompanyitkeeps” (J.R.Firth1957:11)

• OneofthemostsuccessfulideasofmodernstatisticalNLP!

• Whenawordw appearsinatext,itscontext isthesetofwordsthatappearnearby(withinafixed-sizewindow).

• Usethemanycontextsofw tobuilduparepresentationofw

…government debt problems turning into banking crises as happened in 2009……saying that Europe needs unified banking regulation to replace the hodgepodge…

…India has just given its banking system a shot in the arm…

Thesecontextwordswillrepresentbanking 1/11/189

Wordvectors

Wewillbuildadensevectorforeachword,chosensothatitissimilartovectorsofwordsthatappearinsimilarcontexts.

Note:wordvectorsaresometimescalledwordembeddings orwordrepresentations.

linguistics=

0.2860.792−0.177−0.1070.109−0.5420.3490.271

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2.Word2vec:Overview

Word2vec(Mikolov etal.2013)isaframeworkforlearningwordvectors.Idea:

• Wehavealargecorpusoftext• Everywordinafixedvocabularyisrepresentedbyavector• Gothrougheachpositiont inthetext,whichhasacenterword

c andcontext(“outside”)wordso• Usethesimilarityofthewordvectorsforcando tocalculate

theprobabilityofo givenc(orviceversa)• Keepadjustingthewordvectorstomaximizethisprobability

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Word2VecOverview

• Examplewindowsandprocessforcomputing𝑃 𝑤789|𝑤7

…crisesbankingintoturningproblems… as

centerwordatpositiont

outsidecontextwordsinwindowofsize2


𝑃 𝑤78;|𝑤7

𝑃 𝑤78<|𝑤7

𝑃 𝑤7=;|𝑤7

𝑃 𝑤7=<|𝑤7

1/11/1812

Word2VecOverview






𝑃 𝑤78;|𝑤7

𝑃 𝑤78<|𝑤7

𝑃 𝑤7=;|𝑤7

𝑃 𝑤7=<|𝑤7

1/11/1813

Word2vec:objectivefunctionForeachposition𝑡 = 1,… , 𝑇,predictcontextwordswithinawindowoffixedsizem,givencenterword𝑤9.

𝐿 𝜃 =F F 𝑃 𝑤789|𝑤7; 𝜃�

=IJ9JI9KL

M

7N;

Theobjectivefunction 𝐽 𝜃 isthe(average)negativeloglikelihood:

𝐽 𝜃 = −1𝑇log 𝐿(𝜃) = −

1𝑇S S log𝑃 𝑤789|𝑤7; 𝜃

�

=IJ9JI9KL

M

7N;

Minimizingobjectivefunction⟺Maximizingpredictiveaccuracy

Likelihood=

𝜃 isallvariablestobeoptimized

sometimescalledcost orlossfunction

1/11/1814

Word2vec:objectivefunction

• Wewanttominimizetheobjectivefunction:

𝐽 𝜃 = −1𝑇S S log𝑃 𝑤789|𝑤7; 𝜃

�

=IJ9JI9KL

M

7N;

• Question: Howtocalculate𝑃 𝑤789|𝑤7; 𝜃 ?

• Answer: Wewillusetwovectorsperwordw:

• 𝑣U whenw isacenterword

• 𝑢U whenw isacontextword

• Thenforacenterwordc andacontextwordo:

𝑃 𝑜 𝑐 = exp(𝑢YM𝑣Z)

∑ exp(𝑢UM 𝑣Z)�U∈] 1/11/1815

Word2VecOverviewwithVectors






𝑃 𝑢Z_dcbc|𝑣`peqder

𝑃 𝑢pc|𝑣`peqder

𝑃 𝑢de7Y|𝑣`peqder

𝑃 𝑢7s_eder|𝑣`peqder

1/11/1817

Word2vec:predictionfunction

𝑃 𝑜 𝑐 = exp(𝑢YM𝑣Z)

∑ exp(𝑢UM 𝑣Z)�U∈]

• Thisisanexampleofthesoftmax functionℝe → ℝe

softmax 𝑥d = exp(𝑥d)

∑ exp(𝑥9)e9N;

= 𝑝d

• Thesoftmax functionmapsarbitraryvalues𝑥d toaprobabilitydistribution𝑝d• “max” becauseamplifiesprobabilityoflargest𝑥d• “soft” becausestillassignssomeprobabilitytosmaller𝑥d• FrequentlyusedinDeepLearning

Dotproductcomparessimilarityofo andc.Largerdotproduct=largerprobability

Aftertakingexponent,normalizeoverentirevocabulary

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Totrainthemodel:Computeall vectorgradients!

• Recall:𝜃representsallmodelparameters,inonelongvector• Inourcasewithd-dimensionalvectorsand V-manywords:

• Remember:everywordhastwovectors• Wethenoptimizetheseparameters

1/11/1819

3.Derivationsofgradient

• Whiteboard– seevideoifyou’renotinclass;)

• ThebasicLegopiece

• Usefulbasics:

• Ifindoubt:writeoutwithindices

• Chainrule!Ify =f(u)andu =g(x),i.e.y=f(g(x)),then:

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ChainRule

• Chainrule!Ify =f(u)andu =g(x),i.e.y=f(g(x)),then:

• Simpleexample:

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InteractiveWhiteboard Session!

Let’sderivegradientforcenterwordtogetherForoneexamplewindowandoneexampleoutsideword:

Youthenalsoneedthegradientforcontextwords(it’ssimilar;leftforhomework).That’salloftheparametersθ here.

1/11/1822

Calculatingallgradients!

• Wewentthroughgradientforeachcentervectorv inawindow• Wealsoneedgradientsforoutsidevectorsu• Deriveathome!• Generallyineachwindowwewillcomputeupdatesforall

parametersthatarebeingusedinthatwindow.Forexample:





𝑃 𝑢Z_dcbc|𝑣`peqder

𝑃 𝑢pc|𝑣`peqder

𝑃 𝑢de7Y|𝑣`peqder

𝑃 𝑢7s_eder|𝑣`peqder

1/11/1823

Word2vec:Moredetails

Whytwovectors?à Easieroptimization.Averagebothattheend.

Twomodelvariants:1. Skip-grams(SG)

Predictcontext(”outside”)words(positionindependent)givencenterword

2. ContinuousBagofWords(CBOW)Predictcenterwordfrom(bagof)contextwords

Thislecturesofar:Skip-grammodel

Additionalefficiencyintraining:1. Negativesampling

Sofar:Focusonnaïvesoftmax (simplertrainingmethod)

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GradientDescent

• Wehaveacostfunction𝐽 𝜃 wewanttominimize• GradientDescentisanalgorithmtominimize𝐽 𝜃• Idea:forcurrentvalueof𝜃,calculategradientof𝐽 𝜃 ,thentake

smallstepindirectionofnegativegradient.Repeat.

Note:Ourobjectivesarenotconvexlikethis:(

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Intuition

Forasimpleconvexfunctionovertwoparameters.

Contourlinesshowlevelsofobjectivefunction

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• Updateequation(inmatrixnotation):

• Updateequation(forsingleparameter):

• Algorithm:

GradientDescent

𝛼 =stepsizeorlearningrate

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StochasticGradientDescent

• Problem:𝐽 𝜃 isafunctionofall windowsinthecorpus(potentiallybillions!)• Soisveryexpensivetocompute

• Youwouldwaitaverylongtimebeforemakingasingleupdate!

• Very badideaforprettymuchallneuralnets!• Solution: Stochasticgradientdescent(SGD)• Repeatedlysamplewindows,andupdateaftereachone.

• Algorithm:

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PSet1:Theskip-grammodelandnegativesampling

• Frompaper:“DistributedRepresentationsofWordsandPhrasesandtheirCompositionality”(Mikolov etal.2013)

• Overallobjectivefunction(theymaximize):

• Thesigmoidfunction!(we’llbecomegoodfriendssoon)

• Sowemaximizetheprobabilityoftwowordsco-occurringinfirstlogà

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PSet1:Theskip-grammodelandnegativesampling

• Simplernotation,moresimilartoclassandPSet:

• Wetakeknegativesamples.• Maximizeprobabilitythatrealoutsidewordappears,

minimizeprob.thatrandomwordsappeararoundcenterword

• P(w)=U(w)3/4/Z,theunigramdistributionU(w)raisedtothe3/4power(Weprovidethisfunctioninthestartercode).

• Thepowermakeslessfrequentwordsbesampledmoreoften

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PSet1:Thecontinuousbagofwordsmodel

• Mainideaforcontinuousbagofwords(CBOW):Predictcenterwordfromsumofsurroundingwordvectorsinsteadofpredictingsurroundingsinglewordsfromcenterwordasinskip-grammodel

• Tomakeassignmentslightlyeasier:

ImplementationoftheCBOWmodelisnotrequired(youcandoitforacoupleofbonuspoints!),butyoudohavetodothetheoryproblemonCBOW.

1/11/1832

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