A GENEI~LIZEI) RECONSTRUCTION ALGORITHM FOR ELLIPSIS 
RESOLUTION 
Shalom Lappin and ttsue-Hueh Shih 
Department of Linguistics 
School of Oriental and African Studies 
University of London 
Thornhaugh Street, Russell Square 
London WC1H OXG, UK 
shalom@semantics.soas.ac.uk, hh112@eng.cam.ac.uk 
Abstract 
We present an algorithm which assigns interpretations to 
several major types of ellipsis structures through a 
generalized procedure of syntactic reconstrtiction. 
Ellipsis structures are taken to be sequences of lexically 
realized arguments and/or adjuncts of an empty verbal 
head. Reconstruction is characterized as the specification 
of a (partial) correspondence relation between the 
unrealized head verb of an elided clause and its 
argument and adjuncts on one hand, and the head of a 
non-elided antecedent sentence and its arguments and 
adjuncts on the other. The algorithm generates 
appropriate interpretations for cases of VP ellipsis, 
pseudo-gapping, bare ellipsis (stripping), and gapping. It 
provides a uniform computational approach to a wide 
range of ellipsis phenomena, and it has significant 
advantages over several other approaches to ellipsis 
which have recently been suggested in the computational 
and linguistic literature. 
1 Introduction 
Ellipsis structures pose an important 
problem for NLP systems designed to 
provide text understanding or to handle 
dialogue. They contain information which is 
not overtly expressed, but which must be 
recovered through the identification of an 
antecedent. However, unlike pronominal 
anaphora, which is resolved by matching a 
pronoun with an antecedent noun phrase, the 
interpretation of an ellipsis fragment (or 
sequence of fragments) generally involves 
mapping it (them) into a sentential structure 
by association with an antecedent clause. It 
is possible to distinguish two main 
approaches to ellipsis resolution. The first 
seeks to associate an elided construction 
directly with a semantic representation, 
while the second mediates semantic 
interpretation through the reconstruction of 
the syntactic structure of the antecedent. The 
algorithm we propose implements the second 
view of ellipsis, by characterizing ellipsis 
resolution as the specification of a relation of 
(possibly partial) correspondence between the 
lexically unrealized head of an elided clause 
and its arguments and adjuncts as one term 
of the relation, and the realized head of the 
antecedent clause and its arguments and 
adjuncts as the second term. 
The algorithm is a generalized procedure 
for syntactic reconstruction which provides a 
unified way of handling a significant variety 
of ellipsis constructions. It modifies and 
extends the reconstruction strategy for 
handling VP ellipsis suggested in Lappin and 
McCord (1990). The algorithm covers VP 
ellipsis, illustrated in 1, pseudo-gapping (in 
2), bare ellipsis involving sequences of bare 
arguments, adjuncts or both (in 3), and 
gapping (in 4). 
1. John completed his paper before he 
expected to. 
2. John sent the flowers to Lucy betbre 
he did the chocolates. 
3. Bill wrote reviews :for the journal last 
year, and articles this year. 
4. Sam teaches in London, and Lucy in 
Boston. 
It will be a useful component for source 
analysis in machine translation, text 
understanding systems, and discourse 
interpretation systems. 
687 
2 The Reconstruction Algorithm 
Let an ellipsis fragment be a phrase which 
(i) occurs outside of a lexieally realized 
sentence, and (ii) is interpreted as an 
argument or an adjunct of the head verb of 
a non-elided sentence. Let s = <bl,...,bk> (1 
_< k) be a sequence of ellipsis fragments such 
that, for each bi ~ s, b~q immediately follows 
b i. Take s to be maximal in that there is no 
ellipsis fragment, b0 or bk+ 1, not contained in 
s, which immediately precedes or 
immediately follows an element of s. 
A. Identify an antecedent sentence S for s. 
B. Take the head verb of S, A, as the new 
interpreted head of the sentence to be 
constructed from s (we will refer to the new 
head as A'). 
C. Consider in sequence each argument slot 
Slot~ in the SUBCAT list of A. 
1. If there is a phrase C' in s which is of 
the appropriate type for filling Slot i, then fill 
Slot~ in the SUBCAT list of A' with C' and 
remove C' from s. Else, 
2. If Slota is filled by a phrase C, then fill 
Slot, in A' with C, and list C as a new 
argument of A'. Else, 
3. If Sloti is empty in the frame of A, it 
remains empty in the frame of A'. 
4. Construct a list, Arg-List, of the 
phrases which fill the SUBCAT list slots of 
A'. 
D. Construct a list of adjunct phrases for A' 
as follows. 
1. Construct the list L of adjunct phrases 
in s. 
a. If L 4: nil, then for each element 
AdjP' of L, fill an adjunct slot for A' with 
AdjP'. 
2. Consider each adjunct slot of A filled 
by a phrase AdjP. 
a. If there is a phrase AdjP' filling an 
adjunct slot of the same type in A', then 
leave AdjP' in this slot and remove AdjP' 
from s. Else, 
b. Fill an adjunct slot for A' with AdjP, 
and list AdjP as a new adjunct of A'. 
3. Construct a list, Adj-List, of all the 
phrases which fill adjunct slots of A'. 
E. Generate a new syntactic structure as 
follows. 
1. Concatenate Arg-List and Adj-List to 
create a combined list, Ph-List, of the 
phrasal arguments and adjuncts of A'. 
2. Reorder the elements of Ph-List to 
produce a new list, Ord-Ph-List, in which the 
sequence of arguments and adjunct phrases 
corresponds to the order of arguments and 
adjuncts phrases of A. 
3. Construct a new clause headed by A'. 
4. Substitute Ord-Ph-List for the list of 
arguments and adjunct phrases of A' in the 
new structure. 
3 Coverage and Implementation of the 
Algorithm 
At this point, the algorithm has been 
partially implemented in Prolog to apply to 
the output of McCord's English Slot 
Grammar ESG parser (which also runs in 
Prolog) in order to generate reconstructed 
trees for VP ellipsis and pseudo-gapping 
constructions (see McCord et al. (1992) for 
a description of ESG and NLP systems 
which run on top of it). Examples of the 
algorithm's output for theses cases are given 
in 5 and 6. 
VP Ellipsis 
5. John completed the paper before he 
expected to. 
Interpreted VP ellipsis tree. 
f - subj(n) John(l) noun(prop) 
O top complete(2,1,4) verb(fin) 
I I ndet the(3) det(def) 
L I . obj(n) paper(4) noun(cn) 
I t vsubconj before(5,7) subconj 
I I subj(n) he(6) noun(pron) 
I I sccomp expect(7,6,8) verb(fin) 
I 1 comp(inf) preinf(8,6,9 ) preinf 
t f auxcomp complete(9,6,4) verb(inf) 
I I ndet the(3) det(def) 
t J obj(n) paper(4) noun(cn) 
688 
Pseudo-Gapping 
6. John sent the flowers to Mary before he 
did the chocolates. 
Interpreted VP ellipsis tree. 
\[ 
o 
subj(n) 
top 
ndet 
objCn) 
iobj(to) 
objprep 
vsubconj 
, subjCn) 
t sccomp 
I ndet 
t objCn) 
t iobj(to) 
objprep 
John(l) noun(prop) 
send(2,l,4,6) verb(fin) 
the(3) det(det) 
flower(4) noun(cn) 
to(5,6) prep(to) 
Mary(6) noun(prop) 
before(7,9) subconj 
he(8) noun(pron) 
send(9,8,11,6) verb(fin) 
the(l O) det(det) 
chocolate(11) noun(cn) 
to(5,6) prep(to) 
Mary(6) noun(prop) 
The algorithm is currently being re- 
implemented in Prolog to apply to the output 
of a modified ItPSG (Pollard and Sag 
(1994)) grammar designed to handle ellipsis. 
We are developing the grammar within the 
framework of Erbach's (1995) ProFIT system 
for augmenting Prolog with typed feature 
structures. The feature structures which the 
grammar currently generates tbr simple bare 
argument and bare adjunct ellipsis cases are 
illustrated by the AVM's in 7 and 8, 
respectively (cases of bare adverb ellipsis are 
discussed in Chao (1988) and Kcmpson and 
Gabbay (1993)). 
7. John gives Mary flowers, and 
chocolates too. 
phon!\[john, gives, mary, flowers, and, chocolates, tool& 
syn!loc!subcatlH& 
dtrs\[head_dtr\[i)hon! !and } & 
synHoe!head!conj!<conj& 
mtbcat\[\[ QI, O2 I& 
eomp dtrs\[l_Ql& 
phon!ljohn, gives, mary, flowersl& 
syn\[loc\[heaul! .I& 
subeat\[\[\]& 
dtrs\[head_dtr!phon!\[gives, mary, flowers\]& 
synHoelhead!_.l& 
subeat\[\[_Sl& 
dtrslhead_dtr\[phon\[\[gives\]& 
synHoc!head!_J& 
vfm'm!<fin& 
subcat!l_S, _Cl, I | I& 
comp_dtrs\[\[ CI& 
phon\[\[maryl& 
t t 9 l< syn.loc.head.case, ace& 
subeattll, 
II& 
phon!\[flowers\]& 
synHoe!head\[ease\[<acc& 
subeat!\[I 
l& 
comp_dtrs!\[S& 
phon!ljohnl& 
syn!loe!lnead\[case\[<nom& 
subcatlH I, 
02& 
phon!\[choeolates, too\]& 
syn!loe!head\[_Tl& 
subeat!\[l& 
dt rsHtead dtr!phon!\[ehocolates\]& 
syn\[Ioc!head\[ Tl& 
soheat!ll& 
dt rs\[head dtr!phon\[ \[chocolatesl& 
synHoc!head!_TI & 
easel<ace& 
subcattll& 
eomp_dtrs!\[\[& 
eomp_dtrs\[\[\]& 
adj dtrs!phon!ltoo\]& 
syn\[Ioc!head!atype!<too& 
slnbcattll l 
8. John sings, and beautifully too. 
phon!\[john, sings, aml, beautifully, too I& 
syaHoc!subcat\[\[l& 
dt rs\[head dt r!l)hon!\[and\[& 
syn Hoc\[head!cooj!<conj& 
subcat!\[_E 1, W2\]& 
comp dtrs\[\[_El& 
phon!\[john, singsl& 
syn!loc!head! ,l& 
subcat!\[l& 
dtrslheaddtr!phon\[\[sings\]& 
syn!locHlead!_J& 
sobcat!\[ S\]& 
dtrs!head dtr\[phon!bingsl& 
syn\[IocHlead\[ J& 
vfo rm\[<fin& 
subeat!\[_Sl& 
eomp_dt,'s\[ll& 
comp_dtrs!\[ S& 
phoo!ljohn\]& 
synHoc!head!case!<nom& 
subcat!ll I, 
W2& 
phml!\[beautifully, too I& 
sya!loc!hcad!Jl& 
subcat!ll& 
dtrs!head_d trIphon\[lheautifullyl& 
syn!loc!hcad!_Jl& 
sal)cat!\[l& 
! ! ! dtrs.head dtr.phon.\[I)eautifully I & 
syn.loc.head. Jt& 
subcat!\[\]& 
dtrsHtead_dtr!phon! H& 
synHoc!hcad! Jl& 
subeat!ll& 
dtrs!head dtr!phon\[\[\[& 
syn.loe.head, ll & 
vform\[<elided& 
suhcat!\[l& 
eomp_dtrs!ll& 
comp_dtrs\[ n& 
adj dtrs!phon!\[beautifullyl& 
I ! ! y< syo.loc.head.atype, others& 
subcat!\[\]& 
comp_dtrs!H& 
eomp dtrs\[\[l& 
adj_dtrs\[phon!ltoo\]& 
synHocHlead\[atype!<too& 
subeat\[\[I \] 
The bare NP chocolates is the head of the 
689 
elided clause in the second conjunct of 7. 
The generalized ellipsis reconstruction 
algorithm will identify gives as the head V 
of the antecedent clause in the first conjunct, 
and then will fill one of the positions in its 
SUBCAT list with the local features of 
chocolates. If it fills the direct object (third 
complement) position of this list with the 
bare NP, then it will fill the subject and 
indirect object positions with the local 
features of John and Mary, generating the 
reconstructed feature structure corresponding 
to 9. 
9. \[~p \[Np John\] \[vP \[v gives\] \[NP Mary\] 
\[NP flowers\]\]\] and 
\[,, \[Ne John\] \[vP \[vp \[v gives\] \[NP Mary\] 
\[NP chocolates\]\] \[AdvP tOO\]\]\] 
By contrast, the bare adverb beautifully is 
an adjunct daughter of a VP headed by an 
empty verb in 8. This is due to the fact that 
in our grammar, an adverb is an adjunct 
which modifies a VP. The algorithm will 
identify sings as the head V of the 
antecedent clause and substitute it for the 
empty V in 8. This will yield a feature 
structure corresponding to 10. 
10. \[m \[~ John\] \[ve \[v plays\]\]\] and 
\[n,\[NP John\] \[vv \[vp \[vP \[v plays\]\] 
\[AdvP beautifully\]\] too\]\] 
We employ a rule which permits an 
unbounded number of adverbs to be 
generated in successively higher VP's 
through left recursion on the daughter VP 
node. The relevant PS rule is of the form 
VP ~ VP, ADV. We require this rule in 
order to allow for the fact that there is no 
apparent upper bound on the number of 
adverbs in a VP. 11 indicates that it is 
possible to obtain an unbounded number of 
bare adverbial adjuncts in an ellipsis site. 
11 a. John sang, but not in New York. 
b. John sang, but not in New York at the 
concert. 
c. John sang, but not in New York at the 
concert for three hours. 
d. John sang, but not in New York at the 
concert for three hours on Tuesday. 
e. John sang, but not in New York at the 
concert for three hours on Tuesday to 
impress his music teacher. 
4 Comparison with Other Approaches to 
Ellipsis 
Reinhart (1991) suggests a syntactic 
reconstruction account of bare ellipsis which 
adjoins an NP in the antecedent clause to an 
NP fragment by LF movement. The result is 
a conjoined NP which, taken as a 
generalized quantifier, applies to the 
antecedent clause, interpreted as a predicate 
formed by lambda abstraction. So, for 
example, adjunction of ,flowers in the 
antecedent clause of 7 to the NP fragment 
chocolates in the ellipsis site produces the 
LF structure 12a, which is interpreted as 12b. 
12a. \[IP'\[IP John gives Mary tl \] 
\[NP\[NP flowers\], \[NP and \[NP chocolates\]2\]2\]\] 
b. (flowers and chocolates)(~x\[john gives 
mary x\]) 
Given that Reinhart's analysis relies on 
LF adjunction of an NP in the antecedent to 
an NP in the ellipsis site in order to create a 
generalized quantifier corresponding to a 
coordinate NP, it is not clear how it can 
apply to bare ellipsis cases like 3, in which 
a sequence of arguments and adjuncts appear 
in the ellipsis site. Moreover, the analysis 
cannot deal with bare ellipsis cases like 8, 
where a bare adjunct fragment does not 
correspond to any constituent in the 
antecedent clause. Therefore, this account 
does not cover the full range of bare ellipsis 
cases. As we have seen, the proposed 
generalized reconstruction algorithm does 
handle bare ellipsis structures like 8. In cases 
like 3 the algorithm will substitute the head 
V of the antecedent for the empty verb of 
the elided clause, and the bare PP adverb 
will modify the VP headed by this verb. The 
algorithm will fill some of the complement 
positions in the SUBCAT list of the 
reconstructed V with the NP arguments in 
the ellipsis site, and it will fill the remaining 
positions with arguments inherited from the 
antecedent head V. This procedure will yield 
690 
at least one appropriate reconstruction for the 
elided clause. 
Dalrymple et al. (1991) and Shieber et al. 
(1995) present a generalized semantic 
account which employs higher 
order-unification of property and relation 
variables to resolve ellipsis. Their general 
strategy is to specify the interpretation of the 
antecedent clause as an equation between a 
propositional variable S and a predicate- 
argument structure. The arguments of the 
predicate correspond to the fragments in the 
ellipsis site, and ellipsis resolution consists in 
finding an appropriate value for the predicate 
variable which can apply to both the 
sequence of arguments in the interpretation 
of the antecedent clause, and the sequence of 
arguments in the ellipsis site. Given the 
equations in 13a-c, higher-order unification 
correctly generates 13d as the interpretation 
of 3. 
13a. <a~,a2 > = <book reviews,last year> & 
<bj,b2> = <articles,this year> 
b. S l - (wrote book reviews for the 
journal (during) last year)(bill) 
c. R = )~x~,y\[bill wrote x for the 
journal (during) Yl 
d. (book reviews)()~x\[(last year)()~y\[bill 
wrote x for the journal (during) y\])\]) 
and 
(articles)(~,x\[(this year)0~y\[bill 
wrote x for the journal 
(during) y\])\]) 
While the higher-order unification 
analysis can deal with bare ellipsis cases like 
3 (as well as VP ellipsis and 
pseudo-gapping), it is not clear how to apply 
it to bare ellipsis examples like 8, where the 
adjunct in the ellipsis site lacks a 
corresponding element in the antecedent 
clause. Lappin (1996) suggests positing a 
t}ee manner adverbial function variable in 
the lexical semantic representation of verbs 
like sing. This will permit the specification 
of the equations in 14a-c for 8. Higher-order 
unification solves these equations to yield 
14d, the desired interpretation of 8. 
14a. al = fn,,,,or & bl = beautifully 
b. S~ = (J,~,,~,,oXplays))(john) 
c. P = )g\[(/(plays)(john)\] 
d. )vJ\[ff(plays))(john)\]ff,,,~m,,~r ) and 
)g\[(J(plays))(j ohn)\](beautifully) 
In fact, this solution does not generalize 
to cases like 11, which indicate that there is 
no upper bound on the number of 
antecedentless bare adjuncts which can 
appear in a bare ellipsis sequence. As it is 
not possible to posit an unbounded number 
of free adjunct function variables in the 
semantic representation of a verb (VP), it 
seems that the higher-order unification 
analysis cannot deal with these cases. 
The generalized reconstruction algorithm 
presented here does not require the presence 
of constituents in the antecedent 
corresponding to adjunct elements of the 
fragment sequence. When a bare adjunct 
phrase AdjP does not correspond to a phrase 
in the antecedent clause, AdjP is simply 
added to the list of adjuncts of the new head 
verb of the reconstructed clause. Therefore, 
the algorithm produces the correct 
reconstructed forms for the elided clauses in 
ll. 
Another problem is posed by the fact 
that, as higher-order unification applies to 
semantic interpretations of antecedents, it 
will not have access to syntactic structure. 
But at least some cases of ellipsis resolution 
seem to require reference to this structure. 
Consider the contrast between 15a and 15b. 
15a. The studems sent invitations to the 
professors yesterday, and to each 
other today. 
b.??The students said that John sent 
invitations to the professors 
yesterday, and to each other today. 
The elided conjunct in 15b is ill-lbrmed 
because the reciprocal NP each other in the 
bare argument is interpreted as illicitly 
bound from outside of its local syntactic 
domain. By contrast, the generalized 
reconstruction algorithm generates the full 
syntactic structure of the elided clause, and 
so it provides the representation required to 
specify the contrast between 15a and 15b. 
691 
5 Conclusion 
We have proposed a generalized 
reconstruction algorithm for ellipsis 
resolution. The algorithm provides a unified 
computational procedure for assigning 
interpretations to a significant variety of 
ellipsis constructions. The basic strategy 
which the algorithm encodes is to 
reconstruct an elided clause by (i) taking its 
head verb V' to be identical to the head verb 
V of an antecedent clause, (ii) filling the 
argument positions in the SUBCAT list of V' 
with the NP's in the ellipsis site, (iii) 
inheriting NP arguments of V to fill the 
corresponding argument positions in the 
SUBCAT list of V' which the NP's in the 
ellipsis site do not occupy, (iv) applying the 
adjuncts in the ellipsis site to the (possibly 
successive) VP('s) headed by V', and (v) 
inheriting any adjuncts modifying V as 
modifiers of the VP('s) which V' heads, 
when these adjuncts do not correspond to 
adjuncts in the ellipsis site. The algorithm 
has wider empirical coverage than other 
current approaches which have been 
suggested within the computational and 
linguistic literature. It can be integrated into 
a more comprehensive NLP system to 
recover missing information for purposes of 
text and dialogue understanding. 
6 Acknowledgments 
We are grateful to Chris Brew, Jo Calder, 
Claire Grover, and Suresh Manandhar for 
helpful comments on some of the ideas 
proposed here and for useful advice on 
implementational issues. The research 
described in this paper is supported by grant 
GR/K59576 from the Engineering and 
Physical Science Research Council of the 
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