THE XTAG PROJECT AT PENN
Aravind K. Joshi
Departmentof Computer and InformationSiene and
Institute for Researh inCognitiveSiene
University of Pennsylvania
Philadelphia, PA
USA
joshilin.is.upenn.edu
Abstrat
TheXTAGprojetattheUniversityofPennsylvaniahasbeenanon-goingprojetsineabout1988.
At present, the projet onsists of (1) the onstrution of a wide overage lexialized tree-adjoining
grammar (LTAG) for English, relatively smaller grammars for Chinese, Korean, and Hindi, and the
assoiatedparsers,inludingstatistialproessingand(2)extrationofLTAGgrammarsfromannotated
orporaandtheiruseinstatistialparsing,forimprovingannotations,andforross-linguistimappings
usefulformahinetranslation. Thesetwomaindiretionsoftheprojetprovideauniqueenvironmentfor
pursuingseveralformal,linguisti,omputational,andstatistialaspetsofnaturallanguageproessing.
Inthispaperwe will present anintrodutionto LTAG,anoverviewofthe XTAGprojet, andabrief
desriptionofsomespeieorts,espeiallythoserelatedtoparsing.
1 Introdution
TheXTAGprojetattheUniversityofPennsylvaniahasbeenanon-goingprojetsineabout1988.
At present, theprojetonsists of (1) theonstrutionof awide overage lexialized tree-adjoining
grammar(LTAG)forEnglish[XTAGResearhGroup,2001℄andrelativelysmallergrammarsfor
Chi-nese,Korean,andHindi,andtheassoiatedparsers,inludingstatistialproessingand(2)extration
ofLTAGgrammarsfrom annotatedorporaandtheirusein statistialparsing,forimproving
anno-tations,and forross-linguistimappings useful formahine translation. These twomaindiretions
of the projet provide a unique environment for pursuingseveral formal, linguisti, omputational,
andstatistial aspetsof naturallanguageproessing. Inthis paperwewill presentanintrodution
to LTAG, an overview of the XTAG projet, and a briefdesription of somespei eorts,
espe-iallythose relatedto parsing. Theplan ofthepaperisasfollows. InSetion2TAGs(LTAGs) are
introdued, providing adisussionof theimportantissueoflexialization andhowitleadsto TAGs
(LTAGs). InSetion 3someimportantpropertiesof TAGshavebeendesribed. InSetion 4some
seletedeorts(espeiallysomereentones)intheXTAGprojethavebeendisussedbriey,followed
byaonlusionsetion(Setion5).
The earliest stohasti variants of TAG were proposed by [Resnik,1992, Shabes,1992℄. LTAG
grammars have been extrated from annotated orpora[Xiaet al.,2001, Xia,2001, Chiang,2000℄ 1
,
whih in turn have been used for statistial parsing [Chiang,2000, Sarkar,2001℄. The statistial
Thiswork waspartially supported by NSFGrant SBR8920230. Iwant to thankDavid Chiang,Carlos Prolo,
AnoopSarkar,andFeiXiaforprovidingvaluablehelpforthepreparationofthispaper.
1
best derivationforagivensenteneratherthanthebestparsetree.
2 Tree-adjoining grammars
Tree-adjoininggrammar(TAG)isaformaltreerewritingsystem.TAGandLexializedTree-Adjoining
Grammar(LTAG) have been extensively studied both with respet to their formal properties and
to their linguisti relevane. TAG and LTAG are formally equivalent, however, from the linguisti
perspetive LTAG is the system wewill be onerned with in this paper. We will often use these
termsTAGandLTAGinterhangeably.
The motivations for the study of LTAG are both linguisti and formal. The elementary objets
manipulatedbyLTAGarestruturedobjets(treesordiretedayligraphs)andnotstrings. Using
struturedobjetsastheelementaryobjetsoftheformalsystem,itispossibletoonstrutformalisms
whosepropertiesrelatediretlytothestudyofstronggenerativeapaity(i.e.,struturaldesriptions),
whihismorerelevanttothelinguistidesriptionsthantheweakgenerativeapaity(setsofstrings).
Eahgrammarformalismspeiesadomainofloality,i.e.,adomainoverwhihvarious
dependen-ies(syntatiandsemanti)anbespeied. Itturnsoutthatthevariouspropertiesofaformalism
(syntati, semanti, omputational, and even psyholinguisti) follow, to a large extent, from the
initialspeiationofthedomainofloality.
V
NP
CFG G
S NP VP
VP V NP
NP Harry
NP peanuts
VP VP ADV V likes
ADV passionately
S
NP
VP
VP
VP
ADV
NP
NP
V
ADV
peanuts
Harry
likes
passionately
VP
Figure1: Domainof loalityofaontext-freegrammar
2.1 Domain of loality of CFGs
Inaontext-freegrammar(CFG)thedomainofloalityistheoneleveltreeorrespondingtoarule
inaCFG(Fig.1). Itiseasilyseenthattheargumentsofaprediate(forexample,thetwoarguments
oflikes)arenotin thesameloaldomain. Thetwoargumentsaredistributed overthetworules(two
domainsofloality){S!NP VP andVP !V NP. Theyanbebroughttogetherbyintroduing
arule S ! NP V VP. However, then thestruture provided by the VP node is lost. Weshould
alsonote herethat noteveryrule (domain)in theCFG in (Fig. 1) islexialized. The four ruleson
likesandADV bypassionatelythesetworuleswillbeomelexialized. However,therstruleonthe
left (S !NP VP) annot be lexialized. Cana CFGbelexialized, i.e., givena CFG,G, an we
onstrutanotherCFG,G 0
,suhthateveryrulein G 0
islexializedand T(G),theset of(sentential)
trees(i.e.,thetreelanguageofG)isthesameasthetreelanguageT(G 0
)ofG 0
? Itanbeshownthat
thisisnotthease[JoshiandShabes,1997℄. Ofourse,ifwerequirethatonlythestringlanguages
of Gand G 0
be thesame(i.e., theyare weakly equivalent)then any CFGan be lexialized. This
followsfromthefatthatanyCFGanbeputintheGreibahnormalform whereeahruleisofthe
formA!wB1B2:::BnwherewisalexialitemandtheB 0
sarenonterminals. Thelexialization
weareinterestedin requires thetreelanguages(i.e.,theset ofstrutural desriptions)bethesame,
i.e., we are interested in the bf `strong' lexialization. To summarize, a CFG annot be strongly
lexializedbyaCFG.ThisfollowsfromthefatthatthedomainofloalityofCFGisaoneleveltree
orrespondingtoaruleinthegrammar. Note thattherearetwoissuesweareonernedwithhere{
lexializationofeahelementarydomainandtheenapsulationoftheargumentsofthelexialanhor
in the elementary domain of loality. Theseond issue is independent of the rst issue. From the
mathematialpointofviewtherstissue,i.e.,thelexializationoftheelementarydomainsofloality
istheruialone. Weanobtainstronglexializationwithoutsatisfyingtherequirementspeiedin
theseondissue(enapsulationoftheargumentsofthelexialanhor). Ofourse,fromthelinguisti
point of view the seond issue is very ruial. What this means is that among all possible strong
lexializationsweshould hoose onlythose that meettherequirementsoftheseond issue. Forour
disussionsinthispaperwewillassumethatwealwaysmakesuhahoie.
γ:
β
X
β:
X
α:
X
Figure2: Substitution
NP
peanuts
V
NP
S
VP
likes
CFG G
S NP VP
VP V NP
NP Harry
NP peanuts
NP
Harry
V likes
NP
α2
α3
TSG G’
α1
Nowweanaskthefollowingquestion. CanwestronglylexializeaCFGbyagrammarwithalarger
domainofloality? Fig.2andFig.3showatreesubstitutiongrammarwheretheelementaryobjets
(building bloks) are the three trees in Fig. 3and the ombining operation is the treesubstitution
operationshownin Fig. 2. Note that eah tree in thetree substitution grammar(TSG), G 0
is
lexi-alized,i.e., ithasalexial anhor. It iseasily seenthat G 0
indeedstrongly lexializesG. However,
TSGsfailtostronglylexializeCFGsingeneral. Weshowthisbyanexample. ConsidertheCFG,G,
inFig.4andaproposedTSG,G 0
. ItiseasilyseenthatalthoughGandG 0
areweaklyequivalentthey
arenotstronglyequivalent. InG 0
,supposewestartwith thetree
1
thenbyrepeatedsubstitutions
oftreesinG 0
(anodemarkedwithavertialarrowdenotesasubstitutionsite)weangrowtheright
sideof
1
asmuhaswewantbutweannotgrowtheleftside. Similarlyfor
2
weangrowtheleft
sideasmuhaswewantbut nottherightside. However,treesin Gangrowonbothsides. Hene,
theTSG,G 0
,annotstronglylexializetheCFG,G[JoshiandShabes,1997℄.
α1
S
CFG
G
α2
TSG G’
S S S
S
(non-lexical)
(lexical)
S
S
S
S
S
α3
S
a
a
a
a
Figure4: Atreesubstitutiongrammar
X
X*
X
X
X
α
β
γ
β
Figure 5: Adjoining
Wenowintrodueanewoperationalled`adjoining'asshowninFig.5. Adjoininginvolvesspliing
(inserting)onetreeinto another. Morespeially,atree asshowninFig.5isinserted(adjoined)
into thetree at thenode X resulting in the tree. The tree, alled an auxiliary tree, hasa
speial form. The root node is labeled with anonterminal, say X and on thefrontierthere is also
anode labeled X alled the foot node (marked with *). There ould be other nodes (terminal or
nonterminal) nodeson thefrontierof, thenonterminalnodes will be marked assubstitution sites
(withavertial arrow). Thusifthere is anotherourreneofX (otherthanthe foot node marked
α1
S
CFG
G
α2
TSG
G’
S S S
S
S
S
S
α3
S
a
a
a
a
Adjoining a1 at
α3
at the S node and
then adjoining a1 at the root of the
derived tree we have
γ.
S
S
S
S
S
a
a
a
γ
S*
S*
Figure6: Adjoiningarisesoutoflexialization
alsobeseenasapairofsubstitutionsasfollows: ThesubtreeatX inis detahed, is substituted
atX andthedetahedsubtreeisthensubstitutedatthefootnodeof. Atreesubstitutiongrammar
when augmented with the adjoining operation is alled a tree-adjoining grammar (lexialized
tree-adjoininggrammarbeauseeahelementarytreeislexiallyanhored). Inshort,LTAGonsistsofa
niteset ofelementarytrees,eahlexializedwith atleast onelexial anhor. Theelementarytrees
areeither initialorauxiliarytrees. Auxiliarytrees havebeendened already. Initial treesare those
forwhihallnonterminalnodesonthefrontieraresubstitutionnodes. ItanbeshownthatanyCFG
anbestronglylexializedbyanLTAG[JoshiandShabes,1997℄.
InFig.6weshowaTSG,G 0
,augmentedbytheoperationofadjoining, whihstronglylexializes
theCFG, G. Note that the LTAGlooks the sameasthe TSGonsidered in Fig.4. However, now
trees
1 and
2
areauxiliarytrees(markedwith*)thatanpartiipateinadjoining. Sineadjoining
aninsert atree in the interior of anothertree it is possible to grow both sides of the tree
1 and
tree
2
, whih was not possible earlier with substitution alone. In summary, we haveshown that
byinreasingthe domainof loalitywehaveahievedthefollowing: (1) lexializedeahelementary
domain,(2)introduedanoperationofadjoining,whih wouldnotbepossiblewithouttheinreased
domain of loality (note that with one level trees as elementary domains adjoining beomes the
sameassubstitutionsinetherearenointeriornodesto beoperatedupon),and(3) ahievedstrong
lexializationofCFGs.
2.3 Lexialized tree-adjoining grammars
RatherthangivingformaldenitionsforLTAGandderivationsinLTAGwewillgiveasimpleexample
toillustrate somekeyaspetsof LTAG.Weshowsomeelementarytrees ofatoyLTAGgrammar of
English. Fig.7showstwoelementarytreesforaverbsuhaslikes. Thetree
1
is anhoredonlikes
and enapsulatesthe two arguments of theverb. The tree
2
orresponds to theobjet extration
onstrution. Sine weneed to enapsulate all the argumentsof the verb in eah elementary tree
transitive
object extraction
S
NP
VP
V
NP
likes
S
NP(wh)
S
NP
VP
V
NP
likes
ε
α1
α2
Figure7: LTAG:Elementarytreesforlikes
NP
NP
*
*
β1
S
VP
V
think
β2
S
V
does
α3
NP
Harry
who
S
S
α4
α5
NP
Bill
extration,topialization, subjetrelative,objetrelative,passive,et.) there willbeanelementary
treeassoiatedwith that onstrution. By`minimal' we meanwhen allreursion hasbeenfatored
away. This fatoring of reursion away from the domain over whih the dependenies have to be
speiedisaruialaspetofLTAGsastheyareusedinlinguistidesriptions. Thisfatoringallows
alldependeniestobeloalizedin theelementarydomains. Inthissense,therewill, therefore,beno
longdistane dependeniesassuh. Theywillallbeloal andwillbeomelongdistane onaount
oftheompositionoperations,espeiallyadjoining.
NP
NP
NP
NP
S
VP
NP
V
S
V
NP
VP
S
likes
V
S
S*
does
think
S*
who
Harry
Bill
substitution
adjoining
α2
NP(wh)
β1
α3
α4
α5
β2
ε
Figure9: LTAGderivation forwho does Bill thinkHarrylikes
Fig.8showssomeadditionaltrees. Trees
3 ,
4 , and
5
areinitial treesand trees
1 and
2 are
auxiliarytrees with foot nodes markedwith *. A derivation using the trees in Fig. 7and Fig.8 is
shown in Fig.9. The treesfor who andHarry aresubstituted in the treefor likes at therespetive
NP nodes, thetreefor Bill is substitutedin the treefor think at theNP node, the treefor does is
adjoined to the root node of the tree for think tree (adjoining at the root node is aspeial ase of
adjoining),andnallythederivedauxiliarytree(afteradjoining
2 to
1
)isadjoinedtotheindiated
interiorS node of the tree
2
. This derivation results in thederived tree for who does Bill think
HarrylikesasshowninFig.10. Notethat thedependenybetweenwho andtheomplementNP in
2
(loaltothattree)hasbeenstrethedin thederivedtreein Fig.10. Thistreeistheonventional
treeassoiatedwiththesentene.
However,inLTAGthereisalsoaderivationtree,thetreethatreordsthehistoryofompositionof
theelementarytreesassoiatedwiththelexialitemsinthesentene. Thisderivationtreeisshownin
Fig.11. Thenodesofthetreearelabeledbythetreelabelssuhas
2
togetherwiththelexialanhor. 2
Thederivationtreeistheruialderivation strutureforLTAG.Weanobviouslybuild thederived
treefromthederivationtree. Forsemantiomputationthederivationtree(andnotthederivedtree)
2
S
NP
S
VP
S
S
NP
VP
NP
V
NP
V
Harry
V
ε
Bill
who
does
think
likes
Figure 10: LTAGderivedtreeforwhodoesBill think Harrylikes
istheruialobjet. Compositionalsemantis isdened onthederivation tree. Theideaisthat for
eah elementary treethere is asemantirepresentation assoiatedwith it andthese representations
areomposed using thederivation tree. Sinethe semanti representationfor eah elementary tree
is diretlyassoiatedwith the treethere is no need to reprodue neessarilythe internal hierarhy
in the elementary tree in the semanti representation [JoshiandVijay-Shanker,1999℄. This allows
theso-alled `at' semantirepresentation aswellas helps in dealing with somenon-ompositional
aspetsasintheaseofrigidandexibleidioms.
α3
β1
α4
β2
α5
(likes)
(who)
(think)
(Harry)
(does)
(Bill)
00
010
01
0
00
α2
Figure11: LTAGderivationtree
3 Some important properties of LTAG
Thetwo key properties of LTAG are (1) extended domain of loality (EDL) (for example, as
om-paredto CFG), whih allows (2) fatoringreursion from thedomain of dependenies(FRD), thus
making all dependenies loal. All other properties of LTAG (mathematial, linguisti, and even
psyholinguisti)followfrom EDL andFRD. TAGs(LTAGs) belong tothe so-alledlass of mildly
ontext-sensitivegrammars[Joshi,1985℄. Context-freelanguages(CFL)areproperlyontainedinthe
lan-this(and onlythis)languageand thelanguageaeptedbyany EPDA isaTAG language. EPDAs
havebeenusedtomodelsomepsyholinguistiphenomena,forexample,proessingrossed
dependen-iesandnesteddependenieshavebeendisussedin [Joshi,1990℄. With respettoformalproperties,
thelassofTAGlanguagesenjoysalltheimportantpropertiesofCFLs,inludingpolynomialparsing
(withomplexityO(n 6
)).
Large sale wide overage grammars have been built using LTAG, the XTAG system (LTAG
grammar and lexion for English and a parser) being the largest so far (for further details
see[XTAGResearhGroup,2001℄. IntheXTAGsystem,eahnodein eah LTAGtreeis deorated
withtwofeaturestrutures(topandbottomfeaturestrutures),inontrasttotheCFGbasedfeature
struture grammars. This is neessary beause adjoining anaugment a tree internally, while in a
CFGbasedgrammaratreean beaugmentedonly atthefrontier. It ispossibleto deneadjoining
andsubstitution(asitisdoneintheXTAGsystem)intermsofappropriateuniationsofthetopand
bottom feature strutures. Beause of FRD(fatoring reursionfrom the domain of dependenies),
thereisnoreursioninthefeaturestrutures. Therefore,inpriniple,featurestruturesanbe
elimi-nated. However,theyareruialforlinguistidesriptions. Constraintsonsubstitutionandadjoining
aremodeledviathesefeature strutures[Vijay-Shanker,1987℄. Thismethodofmanipulatingfeature
struturesisadiretonsequeneoftheextendeddomainofloalityofLTAG.
4 Stohasti TAGs (LTAGs) and extrated LTAGs
What is the relevane of LTAGs to statistial parsing? It might be thought that itsadded formal
powermakesparameterestimationunneessarilydiÆult; orthatwhateverbenetsitprovides|the
abilitytomodelunboundedross-serialdependenies,forexample|areinonsequentialforstatistial
parsing,whihisonernedwiththeprobableratherthanthepossible.
However,just asTAGisnot,byitself, aomplete linguistitheory, butaformalismforspeifying
linguistitheories,itshouldnotbeviewedasastatistialmodelbutaformalismforspeifying
statis-tialmodels. Theadvantagethat TAGhasoverCFGisthat itassignsriherstrutural desriptions
tosentenes;speially,inadditiontoparsetrees,itassignsderivationtrees (seeSetion2)onwhih
featuresofaparsingmodelanbedened.
[Chiang,2001,Chiang,2000℄givesastatistialparserbasedonstohastiTreeInsertionGrammars,
avariantofTAGsintroduedin[ShabesandWaters,1994℄,whihonstrainstheoperationof
adjoin-inginwaysuhthattheweakgenerativepowerisequivalenttoCFGsbutthestronggenerativepower
(relevant to strutural desriptions) is stritly greater than that for CFGs. The experiments were
basedon fully lexializedelementary trees and ahieves87.6% labeled preisionand 87.4% labeled
reall. These results show that onedoes not have to sarie performane over lexialized PCFGs
while maintaining a more elaborate model using TAGs. [Chiang,2001℄ also reports results on the
ChineseTreebank 3
. Thisinvolvedonlyminorhangesto theEnglishparser.
3
TheChineseTreebank isnot partofthe XTAGprojet. It isa separateprojet. Theworkisbeingarriedout
byFeiXia. TheprojetisdiretedbyMartha Palmer. Thegoal oftheprojetistobuild alarge-salehigh-quality
Treebankfor Chinese. Therstportion ofthe Treebank,whih hasabout 100thousandwords,was releasedtothe
publiin2000. Sinethen, moredata fromvarioussoureshavebeenannotated. Therstportionofthe Treebank
onsistsof325 artilesfromthe Xinhua newswirepublishedin1994-1998(themajorityofthese doumentsfouson
PCFG-based modelsdo (apossibilitynotedearlyon by [Resnik,1992,Shabes,1992℄, but withless
notationaloverhead, and demonstrate that it an be used to parse with omparable auray. He
arguesthattheuseofprobabilistiTAGprovidestwobenetsoverPCFG:rst,itnaturallyaptures
dependenies thatmustbeenoded ad ho into aPCFG, inludingdependenieswhih the
PCFG-basedparsersdonotapture;seond,thederivationtreesaTAGparseromputesinadditiontoparse
treesareusefulforfurtherproessing(forexample,translationorsemantiinterpretation) 4
[Xiaetal.,2001,Xia,2001℄reportsonanalgorithm(LexTrat)thatpermittheextrationofTAG
derivationtreesfromTreebanksinvariouslanguages. Thealgorithmusesonlyminimaleditstotables
ofdatathatareloalizedtoeahnewTreebank. Theextrationproesshasthreesteps: (1)LexTrat
fully brakets eah tree in the Penn Treebank. This is beause in the Penn Treebank `modier'
strutures, in general, are shown as at strutures. (2) LexTrat deomposes the fully braketed
trees into a set of elementary trees of LTAG. (3) LexTrat builds the derivation tree for the fully
braketed trees. Xia also makesa omparison of the extrated grammar with theXTAG grammar
andaross-linguististudyofextratedgrammarsforEnglish,Chinese,andKorean[Xiaet al.,2001℄.
[Sarkar,2001℄explores somenewmahine learningtehniques to enablestatistialparsers totake
advantageofunlabeleddata. Byexploitingthe representationofstohasti TAGto viewparsingas
alassiation task, it uses a mahine learningmethod alled Co-Training to iteratively label new
training data for the parser, improving its performane over simply using the available amount of
labeleddata. Whiletrainingonlyonthelabeledsetgaveaperformaneof72.23%and69.1%labeled
braketingpreisionandreall,thetehniqueahieves80.02%and79.64%labeledbraketingpreision
andreallusing Co-Trainingwith alabeledset ofabout10Ksentenes andanunlabeledset of30K
sentenes. Thisispreliminaryworkandexperimentsarein progressoverlargedatasets.
[Srinivas,1997b, Srinivas,1997a℄ desribesamethod ofpartialparsingthat usesloal attahment
heuristisafteraprobabilistimethodthatpiksthebestelementarytreeforeahwordinasentene:
atehniquetermedasSuperTaggingindiatingtheaÆnitybetweentheproblemsofassigningomplex
struturessuhastreesto eah wordin asenteneasompared totheassignmentofpartofspeeh
tags.
Prolois urrently developingalargesale LR parserforLTAGaiming to produeapratialLR
parserbyrelaxingsomeoftheLRtheoretialassumptions. Heusesorpus-basedstatistialtehniques
toresolveparsingonits[Prolo,2000℄. TheparserisbeingtestedonTAGgrammarsextratedfrom
theEnglishPennTreebank.
5 Conlusion
We havegivenan introdution to LTAG, emphasizing espeially the role of lexialization and how
LTAGsarise fromCFGsinthe proessof lexialization. This aspetof LTAGsis highlyrelevantto
parsing.WehavethenprovidedanoverviewoftheXTAGprojet. ThetwomainaspetsoftheXTAG
projet (a) the onstrution of wide overage grammars and the extration of the grammars from
4
The followingwork byHwa [Hwa,1998 ℄,Harvard Univerity, isnot part of the XTAG projet. We mentionit
herebeauseitishighlyrelevanttotheworkonstohastiTAG.Hwausestheinside-outsidealgorithmforstohasti
Tree Insertion Grammarsdened in [Shabes,1992 ℄and ombines thiswiththe useof inside-outsidetraining from
partiallybraketedTreebankdata[PereiraandShabes,1992 ℄.TheexperimentsreportedwereondutedontheWSJ
PennTreebankwiththeinputtothelearningalgorithmbeingpart-of-speehtags(ratherthanthewordsthemselves).
for pursuing several formal, linguisti, omputational, and statistial aspets of natural language
proessing. Wehavealsodesribedsomespeieorts,espeiallythoserelatedtoparsing.
Referenes
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