Interleaved semantic interpretation in environment-based parsing
WilliamSchuler
Computer and Information Science Dept.
University of Pennsylvania
Philadelphia, PA 19103
Abstract
This paper extends a polynomial-time parsing
al-gorithmthat resolvesstructuralambiguityin input
sentences by calculating and comparing the
deno-tations of rival constituents, given some model of
the application environment (Schuler, 2001). The
algorithm is extended to incorporate a full set of
logicaloperators,includingquantiersand
conjunc-tions, into this calculation without increasing the
complexityof theoverall algorithmbeyond
polyno-mial time, both in terms of the length of the
in-put and the numberof entities in the environment
model.
1 Introduction
The development of speaker-independent
mixed-initiative speech interfaces,in whichusers notonly
answerquestionsbutalsoaskquestionsandgive
in-structions, is currently limited by the inadequacy
ofexistingcorpus-baseddisambiguationtechniques.
This paper explores the use of semantic and
prag-matic information, in the form of the entities and
relationsintheinterfacedapplication'srun-time
en-vironment,asanadditionalsourceofinformationto
guidedisambiguation.
Inparticular,thispaperextendsanexisting
pars-ingalgorithm that calculatesand comparesthe
de-notations of rival parse tree constituents in order
to resolve structural ambiguity in input sentences
(Schuler,2001). Thealgorithmisextendedto
incor-porateafullsetof logicaloperatorsintothis
calcu-lationsoastoimprovetheaccuracyoftheresulting
denotations{ andthereby improve theaccuracyof
parsing{ without increasing the complexity of the
overall algorithm beyond polynomialtime (bothin
termsofthe lengthof theinputand thenumberof
entitiesintheenvironmentmodel). Thisparsimony
is achieved by localizing certain kinds of semantic
relationsduringparsing,particularlythosebetween
quantiersandtheirrestrictorandbodyarguments
TheauthorwouldliketothankDavidChiang,Karin
Kip-per,andAlexanderKoller,aswellastheanonymousreviewers
forcommentsonthismaterial. Thisworkwaspartially
sup-(similartothewaydependenciesbetweenpredicate
andargumentheadwordsarelocalizedinlexicalized
formalismssuchastreeadjoining grammars),in
or-dertoavoidcalculatingexponentialhigher-order
de-notationsforexpressionslikegeneralizedquantiers.
2 Basic algorithm
Thissection describes thebasicenvironment-based
parser(Schuler,2001)whichwillbeextendedin
Sec-tion3. Becauseitwill cruciallyrelyonthe
denota-tions (or interpretations) of proposed constituents
inordertoguidedisambiguation,theparserwill be
denedoncategorialgrammars(Ajdukiewicz,1935;
Bar-Hillel,1953),whose categoriesallhavewell
de-ned typesand worst-case denotations. These
cat-egoriesare drawn from aminimal set of symbolsC
suchthat:
NP2CandS2C;
if;Æ2C then=Æ2CandnÆ2C:
Intuitively,thecategoryNPdescribesanounphrase
and the category S describes a sentence, and the
complexcategories=ÆandnÆdescribe`alacking
a Æ to the right' and `a lacking a Æ to the left'
respectively;so forexampleSnNPwoulddescribea
declarativeverbphraselackinganNPsubjecttoits
leftintheinput.
ThetypeTandworst-case(mostgeneral)
denota-tionW ofeachpossiblecategoryaredenedbelow,
givenasetofentitiesE asanenvironment:
T(S)=t:truthvalue W(S)=fTRUE;FALSEg
T(NP)=e:entity W(NP)=E
T(=Æ )=hT(Æ);T()i W(=Æ)=W(Æ)W()
T(nÆ )=hT(Æ);T()i W(nÆ)=W(Æ)W()
The denotation D of any proposed constituent is
constrained to be a subset of the worst-case
deno-tation W of the constituent's category; so a
con-stituent of category NP would denote a set of
en-tities, fe
1 ;e
2
;:::g, and a constituent of category
SnNP would denote a set of entitytruth value
pairs,fhe
1
;TRUEi;he
2
O(jEj )dierentelements,wherevisavalency
mea-sureofthenumberofNP symbolsoccurringwithin
theconstituent'scategory.
This paper will use the following denition of a
categorialgrammar(CG):
Denition A categorial grammar G is a formal
grammar(N;;P)suchthat:
isaniteset ofwordsw;
P isaniteset ofproductionscontaining:
!w forallw2,with2C,
!=Æ Æ foreveryrule=Æ!::: inP,
!Æ nÆ foreveryrulenÆ!::: inP,
andnothingelse;
N is thenonterminalsetfj!::: 2Pg.
and the following deductive parser, 1
which will be
extendedlatertohandlearichersetofsemantic
op-erations. Theparserisdened with:
constituentchartitems[i;j;]drawnfromI n
N,indicatingthatpositionsithroughj in
theinputcan becharacterizedbycategory;
alexicalitem[i;j;]foreveryrule!w2P if
woccursbetweenpositionsiandjintheinput;
aset ofrulesof theform:
andcanrecognizeann-lengthinputasaconstituent
ofcategory(forexample,asanS)ifitcandeduce
thechartitem[0;n;].
Thisparsercanbeimplementedinadynamic
pro-grammingalgorithm,usingtherecursivefunction:
F(x)=
are proposedconstituentsdrawn
from I
byrecordingtheresultofeveryrecursivesub-callto
F(x)in achart,then consultingthis charton
sub-sequent calls to F(x) for the same x constituent. 2
Since the indices in every rule's antecedent
con-stituents a
1 :::a
k
each cover smaller spans than
those in the consequent x, the algorithm will not
enter intoan inniterecursion;and since there are
onlyn 2
jNj dierentvaluesofx,andonly2n
dier-entrulesthatcouldproveanyconsequentx(tworule
formsfor=andn,eachwithndierentvaluesofk),
the algorithm runs in polynomial time: O(n 3
jNj).
Theresultingchartcanthenbeannotatedwithback
pointerstoproduceapolynomial-sizedsharedforest
1
FollowingShieberetal.(1995).
2
FollowingGoodman(1999).
representationofallpossiblegrammaticaltrees
(Bil-lotandLang,1989).
Traditional corpus-based parsers select preferred
treesfrom suchforestsbycalculatingViterbiscores
foreach proposed constituent, according to the
re-cursivefunction:
Thesescores canbecalculated in polynomialtime,
usingthesamedynamic programmingalgorithm as
that described for parsing. A tree can then be
se-lected,fromthetopdown,byexpandingthe
highest-scoringruleapplicationforeachconstituent.
Theenvironment-basedparserdescribedhereuses
asimilarmechanismtoselectpreferredtrees,butthe
scoresarebasedonthepresenceorabsenceof
enti-ties in the denotation(interpretation) of each
pro-posedconstituent:
wherethedenotationD(x)ofaproposedconstituent
xiscalculatedusinganotherrecursivefunction:
D(x)=
in which R (x)is a lexical relation dened for each
axiomx of category equal to some subset of 's
worst-casedenotationW(),asdenedabove. 4
The
operator onis natural(relational) join onthe elds
ofitsoperands:
AonB=fhe
the rst element of the result (corresponding the
most recently discharged argument of the head or
functorcategory):
This interleaving of semantic evaluation and
pars-ingfor the purpose of disambiguationhas much in
commonwith thatofDowdingetal.(1994),except
3
Here, the scoreis simplyequal to the numberof
non-emptyconstituentsinananalysis,butothermetricsare
pos-sible.
4
So a lexical relation for the constituent `lemon' of
category NP would contain all and only the lemons in
the environment, and a lexical relation for the
con-stituent `falling' of category SnNP would contain a
map-ping from every entity in the environment to some truth
value (TRUE if that entity is falling, FALSE otherwise):
NP[lemon]
fl
1 ;l
2 ;l
3 ;l
4 g
P:NPnNP/NP[in]
fhb
1 ;hl
1 ;l
1 ii;hm
1 ;hl
2 ;l
2 iig
NP[bin]
fb
1 ;b
2 g
P:NPnNP/NP[by]
fhm
1 ;hb
1 ;b
1 ii;hm
2 ;hb
2 ;b
2 iig
NP[machine]
fm
1 ;m
2 ;m
3 g PP:NPnNP[in]
fhl
1 ;l
1 ig
PP:NPnNP[by]
fhb
1 ;b
1 i;hb
2 ;b
2 ig NP[lemon]
fl1g
NP[bin]
fb1;b2g PP:NPnNP[in]
fhl1;l1ig fl
1 g[;
Figure1: Denotation-annotatedforestfor`lemonin binbymachine.'
that in this case, constituents are not only
seman-tically type-checked, but are also fully interpreted
eachtimetheyareproposed.
Figure 1 shows a sample denotation-annotated
forest for the phrase `the lemon in the bin by the
machine',usingthelexicalizedgrammar:
lemon, bin,machine:NP
the:NP=NP
in,by:NPnNP=NP
inwhichthedenotationofeachconstituent(theset
in each rectangle) is calculatedusing a join on the
denotationsofeachpairofconstituentsthatcombine
to produceit. In thisexample, theright-branching
tree would be preferredbecause thedenotation
re-sultingfromthecompositionattherootoftheother
treewouldbeempty.
Sincethisuseofthejoinoperationislinearonthe
sum of the cardinalities of its operands, and since
the denotations of the categories in a grammar G
areboundedincardinalitybyO(jEj v
)wherevisthe
maximum valency of the categories in G, the total
complexityofthe abovealgorithmcan beshownto
beO(n 3
jEj v
): polynomialnotonlyonthelengthof
theinputn,butalsoonthesizeoftheenvironmentE
(Schuler,2001).
3 Extended algorithm
Theabovealgorithm works well forattaching
ordi-narycomplementsandmodiers,butasasemantic
theoryitisnotsuÆcientlyexpressivetoproduce
cor-rectdenotationsinallcases.Forexample,thelexical
relations dened aboveare insuÆcient to represent
phrase`theboywithnobackpack.' 5
Asimilar
prob-lemoccurswithconjunctions;forexample,theword
`and'(usingcategoryNPnNP=NP)inthephrase`the
child wearing glasses and blue pants', also cannot
beproperlyrepresentedasalexical relation. 6
This
raises the question: how much expressivity can be
allowedinasharedsemanticinterpretationwithout
exceeding the tractable parsing complexity
neces-saryforpracticalenvironment-basedparsing?
In traditional categorial semantics (Montague,
1973;BarwiseandCooper,1981;KeenanandStavi,
1986)quantiersand noun phraseconjunctions
de-note higher-order relations: that is, relations
be-tween whole sets of entities instead of just
be-tween individuals. Under this interpretation, a
quantier like `no' would denote a set of pairs
fhA
1 ;B
1 i;hA
2 ;B
2
i;:::gwhere each A
i and B
i are
disjoint subsets of E, corresponding to an
accept-ablepair of restrictor and body sets satisfying the
quantier`no'. Unfortunately,sincethecardinalities
ofthesehigher-orderdenotationscanbeexponential
onthesizeoftheenvironmentE(thereare2 jEj
pos-siblesubsetsofE and2 2jEj
possiblecombinationsof
twosuch subsets),such anapproachwoulddestroy
thepolynomialcomplexityoftheenvironment-based
parsingalgorithm.
5
Assigning the identityrelation fhe1;e1i;he2;e2i;:::g to
thequantierwouldincorrectlyyield theset ofboyswith a
backpackasadenotationforthefullnounphrase;and
assign-ingtheconverserelation(fromeachentityintheenvironment
toeveryotherentityfhe
1 ;e
2 i;he
1 ;e
3
i;:::g)wouldincorrectly
yieldthesetofboyswithanythingthatisnotabackpack.
6
Theidentityrelationfhe
1 ;e
1 ;e
1 i;he
2 ;e
2 ;e
2
i;:::g,which
yields a correct interpretation in verb phrase conjunction,
would yield an incorrect denotation for the noun phrase
functions is restricted to a nite set (say, to some
subset of words in alexicon), it becomestractable
to store them by name rather than by denotation
(i.e. assets). Such function canthen discharge all
their rst-order arguments in a single derivational
steptoproducearst-orderresult,inordertoavoid
generatingorevaluatinganyhigher-orderpartial
re-sults. Syntactically,thiswouldbeanalogousto
com-posingaquantierwithbothanounphrase
restric-torandabodypredicate(e.g.averborverbphrase)
at the same time, to produce another rst-order
predicate (e.g. a verb phrase or sentence). Since
ageneralizedquantierfunction merelycountsand
comparesthecardinalities ofitsargumentsina
lin-eartimeoperation,thisanalysisprovidesatractable
shortcutto theexponentialcalculationsrequiredin
theconventionalanalysis.
Note that this analysis by itself does not admit
productivemodicationofquantiers(becausetheir
functionsaredrawnfromsomeniteset)orof
quan-tied nounphrases(becausetheyarenolonger
de-rivedasapartial result). Thiscausesnodisruption
totheattachmentofnon-conjunctivemodiers,
be-causeordinarysyntacticmodiersofquantier
con-stituents are seldom productive (in the sense that
their composition does not yield functions outside
someniteset),and syntacticmodiersof NP
con-stituentsusuallyonlymodifytherestrictorsetofthe
quantierratherthantheentirequantiedfunction,
and can therefore safely be taken to attach below
thequantier,totheunquantied NP.
But this is not true in cases involving
conjunc-tion. Conjoined quantiers,like`somebut notall,'
cannotalwaysbedenedusingasinglestandard
lex-ical function; and conjunctions of quantied noun
phrases, like `one orange and one lemon', cannot
beappliedto unquantiedsubconstituents
(syntac-tically, becausethis would fail to subsume the
sec-ond quantier, and semantically, because it is not
the restrictor sets which are conjoined). Keenan
and Stavi (1986)model conjunctions of quantiers
andquantiednounphrasesusinglatticeoperations
onhigher-ordersets,but aspreviouslystated,these
higher-order sets preclude tractable interleaving of
semanticinterpretationwithparsing.
Thesolutionproposedhereistotreateach
quan-tierorquantiednounphraseconjunctionasan
el-lipticalconjunctionoftwocompleterst-order
pred-icates(e.g.verbphrasesorsentences),each
subsum-ingadierentquantierandnounphraserestrictor
(in thecaseofNP conjunction),but sharingor
du-plicating a common body predicate. This analysis
requires multiple components to keep track of the
duplicated material above the conjunction, but as
longasthe numberof components is bounded, the
containing
(duplicated)
oneorange
(unduplicated)
and onelemon
(unduplicated)
Figure2: DuplicatedverbinNPconjunction.
retained. 7
Figure2showsaduplicatedverbpredicateinthe
derivation of an NP conjunction. The conjoined
constituents (the shaded regions in the gure) are
each composed oftwocomponents: oneforthe NP
itself, containing the quantier and the restrictor
predicate, and one for the verb which supplies the
bodypredicateofthequantier. Sincetheconjoined
constituents both correspond to complete
quanti-erexpressions withnounsatisedrst-order
argu-ments,theircategoriesarethat ofsimplerst-order
predicates(they are each complete verbphrases in
essence: `containingoneorange'and`containingone
lemon'). The conjunction then forms alarger
con-stituent of the same form (the unshaded outline
in the gure), with a lower component containing
theconjoinedconstituents'NPcomponents
concate-natedintheusualway,andanuppercomponentin
which the conjoined constituents' non-NP
compo-nentsareidentiedoroverlapped. Iftheduplicated
componentsdonotcoverthesamestringyield,the
conjunctiondoesnotapply.
Notethat,sincetheyareonlyappliedtoordinary
rst-orderpredicates(e.g.sentencesorverbphrases)
in thisanalysis,conjunctions cannowsafely be
as-signed the familiar truth-functional denotations in
every case. 8
Also, since the resulting constituent
hasthesamenumberofcomponentsastheconjoined
constituents, there is nothing to prevent its use as
anargumentin subsequentconjunctionoperations.
Asamplemulti-componentanalysisforquantiers
isshown below, allowing materialto be duplicated
bothtotheleftandtotherightofaconjoinedNP:
some,all,no,etc.:XnNP
q NP
q nNP
q NP
q =NP
X=NP
q NP
q nNP
q NP
q =NP
Thelexicalentryforaquantiercanbesplitinthis
7
Dahl andMcCord (1983) propose a similarduplication
mechanismtoproduce appropriatesemanticrepresentations
forNPandotherconjunctions,butfordierentreasons.
8
e.g. for the word `and': fh:::TRUE;:::TRUE;:::TRUEi;
est)of whichis notduplicated inconjunctionwhile
others may or may not be. These include a
com-ponentforthequantierNP
q =NP
(whichwill
ulti-matelyalsocontainanounphraserestrictorof
cate-goryNP
),acomponentforrestrictorPPsand
rela-tiveclausesofcategoryNP
q nNP
q
that areattached
abovethequantierandduplicated inthe
conjunc-tion,andacomponentforthebody(a verborverb
phrase or other predicate) of category XnNP
q or
X=NP
q
. The subscript q species one of a nite
set of quantiers, and the subscript indicates an
unquantiedNP.
The deductive parser presented in Section 2can
nowbeextendedbyincorporatingsequencesof
rec-ognizedandunrecognizedcomponentsinto the
con-stituent chart items. As constituents are
com-posed, components are shifted from the
unrecog-nized sequence
1
c
to the recognized sequence
hi
i, until the unrecognized
se-quenceisempty.
Theextendedparserisdened with:
chartitems of theform [i;j;;], where is
asequenceof unrecognizedcomponents, is
a sequence of recognized components ha;b;i,
and i;j;k;a;b;c areindices in theinput. Each
characterizedbythecategories
1
respectively,so
thatifthesespansareconcatenatedinwhatever
ordertheyoccurin theinputstring,theyform
a grammatical constituent of category with
unrecognizedcomponents.
alexicalitem[i;j;;hi;j;i]foreveryrule!
Tworulesto invokeleft andrightfunction
ap-plicationto anexisting component:
[i;k ;=Æ;hi;k ;=Æi][k ;j;Æ;hk ;b;Æ="i]
Tworulesto invokeleft andrightfunction
ap-plicationto afreshcomponent:
[i;k ;=Æ;hi;k ;=Æi] [k ;j;=ÆÆ;]
Tworulesto dischargeemptycomponents:
[i;j;=ÆÆ;]
[i;j;;]
[i;j;nÆÆ;]
[i;j;;]
Three rules to skip conjunctions, by adding a
ate apartial result of categoryConj 0
Æ
, and the
lattertwousethisto skiptheopposingNP):
[k ;j;Æ;]
[i;j;Conj 0
Æ ;]
[i;k ;Conj ;hi;k ;Conji]
[k ;j;Conj
Two rules to reassemble discontinuous
con-stituents(again,usingapartialresultConj 0
Æ to
reduce thenumberofrangingvariables):
[a;c;Conj;ha;c;Conji][i;j;;hc;b;Æi]
Tworulestocombine adjacentcomponents:
[i;j;;ha;c;Æ="ihc;b;"i]
[i;j;;ha;b;Æi]
[i;j;;hc;b;Æn"iha;c;"i]
[i;j;;ha;b;Æi]
Andoneruletoapplyquantierfunctions:
[i;j;;ha;b;Æ
q i]
[i;j;;ha;b;Æi]
The parsingandscoring functions remain
identi-cal to those in Section 2, but an additional k =1
casecontaining amodied projection function is
now added to the interpretation function, in order
to make the denotations of quantied constituents
dependontheirassociatedquantiers:
D(x)=
Themodiedprojectionfunction evaluatesa
quan-tier function q on some argument denotation A,
comparing the cardinality of the image of the
re-strictorsetinAwiththethecardinalityofimageof
theintersectedrestrictorandbodysets inA: 9
Thisalgorithmparsesacategorialgrammarinthe
usual way { constituentsare initially added to the
chartassinglecomponentscoveringacertain yield
in the input string (the indices of the component
arethesameastheindicesoftheconstituentitself),
and theyare combinedby concatenatingtheyields
ofsmallerconstituentstomakelargerones{untila
conjunction is encountered. Whenaconjunction is
0 1 2 3 4
Figure3: Sample derivationofconjoinedNP.
encounteredimmediatelytotheleftorrightofa
rec-ognizedconstituentconstituentx,andanother
con-stituent of thesamecategoryis foundimmediately
beyond that conjunction, the parser creates a new
constituentthathasthecombinedyieldofboth
con-stituents,butcopiesx'scomponentyield(thestring
indicesof x'soriginalcomponents)with nochange.
Thishastheeect ofcreatingtwonewconstituents
every timetwo existing constituents areconjoined:
eachwithadierentcomponentyield,butbothwith
the same(combined) constituent yield. These new
discontinuous constituents (with component yields
thatdonotexhausttheirconstituentyields)arestill
treatedasordinaryconstituentsbytheparser,which
combines them with argumentsand modiers until
all of their argument positions have been
success-fullydischarged,atwhichpointpairsof
discontinu-ousconstituentswiththesameconstituentyieldcan
bereassembledinto whole {oratleast less
discon-tinuous{constituentsagain.
A sample derivation for the verb phrase
`con-taining one orange and one lemon,' involving
con-junction of existentiallyquantiednoun phrases,is
shown in Figure3, usingthe aboveparse rulesand
thelexicalizedgrammar:
containing:SnNP
q
orange,lemon:NP
First the parser applies the skip conjunction rules
to obtain the discontinuous constituents shown
af-tersteps(1)and(2),andacomponentisdischarged
from each of the resulting constituents using the
empty component rule in steps (3) and (4). The
constituentsresultingfrom(3)and(4)arethen
com-posed with the verbconstituent for `containing' in
steps(5)and (6),usingthe leftattachment rulefor
freshcomponents. The quantiersare then applied
in steps(7) and (8), and theresultingconstituents
arereassembledusing theconjunction rulesin step
(9). The adjacent components in the constituent
resulting from step (9) are then merged using the
combinationruleinstep(10),producingacomplete
gaplessconstituentfortheentire input.
Sincetheparserrulesarexed,andthenumberof
componentsin anychartconstituentisboundedby
themaximumnumberofcomponentsin acategory
(inasmuch as the rules can only add a component
to the recognized list by subtracting one from the
unrecognizedlist),the algorithmmust run in
poly-nomial space and time on the length of the input
sentence. Sincethecardinalityofeachconstituent's
denotation is bounded by jEj v
(where E is the set
of entities in the environment and v is the
maxi-mumvalencyofanycategory),thealgorithmrunsin
worst-casepolynomialspaceonjEj;and sincethere
isnomorethan oneset composition operation
per-formedwhenaruleisapplied,andeachcomposition
ation),thealgorithmruns inworst-casepolynomial
timeonjEjaswell.
4 Evaluation
The extended parser described abovehas been
im-plemented and evaluated on a corpus of 340
spo-ken instructionsto simulated human-like agents in
acontrolled3-Denvironment(that ofchildren
run-ning alemonadestand, which wasdeemedsuitably
familiar to undergraduate student subjects). The
parser was run on the word lattice output of an
o-the-shelfspeechrecognizer(CMUSphinxII)and
theparserchartwasseededwitheveryhypothesized
word. The parser wasalso compared with the
rec-ognizerbyitself,inordertodeterminethedegreeto
which an environment-based approach could
com-plementcorpus-baseddisambiguation. Thesystems
wereevaluatedaswordrecognizers(i.e.ignoringthe
bracketsin the parseroutput) ontherst 100
sen-tencesofthecorpus(correspondingtotherstseven
of 33 subjects); the latter 240 sentences were
re-servedfortrainingtherecognizerandfordeveloping
thegrammarandsemanticlexicon.
Theaverage utterancelength wasapproximately
threeseconds(subsumingabout300framesor
posi-tionsin the parserchart), containinganaverageof
ninewords. Parsingtimeaveragedunder40seconds
persentence ona P4-1500MHz,most ofwhich was
spent in forestconstruction ratherthan denotation
calculation.
Accuracyresultsshowthattheparserwasableto
correctlyidentifyasignicantnumberofwordsthat
therecognizer missed (andvice versa), such that a
perfect synthesis of the two (choosing the correct
wordifitisrecognizedbyeithersystem)would
pro-duce anaverage of 8percentage pointsmore recall
than the recognizer by itself on successful parses,
andasmuchas19percentagepointsmoreforsome
subjects: 10
recognizer parser joint
subject prec recall fail prec recall recall
0 76 79 18 72 74 92
1 77 75 28 63 55 83
2 70 71 33 49 54 69
3 71 67 43 49 45 69
4 66 54 37 44 39 67
5 53 52 54 36 31 72
6 84 84 50 56 63 83
all 68 67 37 53 50 75
which indicates that the environment may oer a
useful additional source of information for
disam-biguation. Thoughitmaynotbepossibleto
imple-ment a perfect synthesis of the environment-based
10
Successful parses are those that result in one or more
completeanalysesoftheinput,evenifthecorrecttreeisnot
above gains can be realized, it would mark a
sig-nicantadvance.
5 Conclusion
This paper has described an extension to an
environment-basedparsingalgorithm,increasingits
semanticcoveragetoincludequantierand
conjunc-tion operations without destroying its polynomial
worst-casecomplexity. Experimentsusingan
imple-mentation of this algorithm on a corpusof spoken
instructionsindicatethat 1) theobserved
complex-ityofthealgorithmissuitableforpracticaluser
in-terfaceapplications, and 2) the ability to draw on
this kind of environment information in an
inter-faced application has the potential to greatly
im-prove recognition accuracy in speaker-independent
mixed-initiativeinterfaces.
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