A CENTERING APPROACH TO PRONOUNS 
Susan E. Brennan, Marilyn W. Friedman, Carl J. Pollard 
Hewlett-Packard Laboratories 
1501 Page Mill Road 
Palo Alto, CA 94304, USA 
Abstract 
In this paper we present a formalization of the center- 
ing approach to modeling attentional structure in dis- 
course and use it as the basis for an algorithm to track 
discourse context and bind pronouns. As described 
in \[GJW86\], the process of centering attention on en- 
tities in the discourse gives rise to the intersentential 
transitional states of continuing, re~aining and shift- 
ing. We propose an extension to these states which 
handles some additional cases of multiple ambiguous 
pronouns. The algorithm has been implemented in 
an HPSG natural language system which serves as 
the interface to a database query application. 
1 Introduction 
In the approach to discourse structure developed in 
\[Sid83\] and \[GJW86\], a discourse exhibits both global 
and local coherence. On this view, a key element 
of local coherence is centering, a system of rules 
and constraints that govern the relationship between 
what the discourse is about and some of the lin- 
guistic choices made by the discourse participants, 
e.g. choice of grammatical function, syntactic struc- 
ture, and type of referring expression (proper noun, 
definite or indefinite description, reflexive or per- 
sonal pronoun, etc.). Pronominalization in partic- 
ular serves to focus attention on what is being talked 
about; inappropriate use or failure to use pronouns 
causes communication to be less fluent. For instance, 
it takes longer for hearers to process a pronominal- 
ized noun phrase that is no~ in focus than one that is, 
while it takes longer to process a non-pronominalized 
noun phrase that is in focus than one that is not 
\[Gui85\]. 
The \[GJW86\] centering model is based on the fol- 
lowing assumptions. A discourse segment consists of 
a sequence of utterances U1 ..... U,~. With each ut- 
terance Ua is associated a list of forward.looking cen- 
~ers, Cf(U,), consisting of those discourse entities 
that are directly realized or realized I by linguistic ex- 
pressions in the utterance. Ranking of an entity on 
this list corresponds roughly to the likelihood that it 
will be the primary focus of subsequent discourse; the 
first entity on this list is the preferred cen~er, Cp(U, O. 
U,~ actually centers, or is "about", only one entity at 
a time, the backward-looking cen~er, Cb(U=). The 
backward center is a confirmation of an entity that 
has already been introduced into the discourse; more 
specifically, it must be realized in the immediately 
preceding utterance, Un-1. There are several distinct 
types of transitions from one utterance to the next. 
The typology of transitions is based on two factors: 
whether or not the center of attention, Cb, is the same 
from Un-1 to Un, and whether or not this entity co- 
incides with the preferred center of U,~. Definitions 
of these transition types appear in figure 1. 
These transitions describe how utterances are 
linked together in a coherent local segment of dis- 
course. If a speaker has a number of propositions to 
express, one very simple way to do this coherently 
is to express all the propositions about a given en- 
tity (continuing) before introducing a related entity 
1U directly realizes c if U is an utterance (of some phrase, 
not necessarily a full clause) for which c is the semantic in- 
terpretation, and U realizes c if either c is an element of the 
situation described by the utterance U or c is directly real- 
ized by some subpart of U. Realizes is thus a generalization of 
directly realizes\[G JW86\]. 
155 
cK~)= cM~) 
cKu.) # cv(~.) 
Cb(U.) = Cb(U._,) Cb(U.) # Cb(U._,) 
CONTINUING 
RETAINING 
SHIFTING 
Figure 1 : Transition States 
(retaining) and then shifting the center to this new 
entity. See figure 2. Retaining may be a way to sig- 
nal an intention to shift. While we do not claim that 
speakers really behave in such an orderly fashion, an 
algorithm that expects this kind of behavior is more 
successful than those which depend solely on recency 
or parallelism of grammatical function. The inter- 
action of centering with global focusing mechanisms 
and with other factors such as intentional structure, 
semantic selectional restrictions, verb tense and as- 
pect, modality, intonation and pitch accent are topics 
for further research. 
Note that these transitions are more specific than 
focus movement as described in \[Sid83\]. The exten- 
sion we propose makes them more specific still. Note 
also that the Cb of \[GJW86\] corresponds roughly to 
Sidner's discourse focus and the Cf to her potential 
foci. 
The formal system of constraints and rules for cen- 
tering, as we have interpreted them from \[GJW86\], 
are as follows. For each \[7, in \[71,..., U,n: 
• CONSTRAINTS 
1. There is precisely one Cb. 
2. Every element of Cf(Un) must be realized 
in U,. 
3. Cb(Un) is the highest-ranked element of 
Cf(U,-1) that is realized in U,. 
• RULES 
1. If some element of Cf(U,-1) is realized as 
a pronoun in U,, then so is Cb(U,). 
2. Continuing is preferred over retaining 
which is preferred over shifting. 
As is evident in constraint 3, ranking of the items 
on the forward center list, Cf, is crucial. We rank the 
items in Cf by obliqueness of grammatical relation of 
the subcategorized functions of the main verb: that 
is, first the subject, object, and object2, followed by 
other subcategorized functions, and finally, adjuncts. 
This captures the idea in \[GJW86\] that subjecthood 
contributes strongly to the priority of an item on the 
C/list. 
CONTINUING... Un+l: 
Carl works at tIP on the Natural Language 
Project. 
Cb: \[POLLARD:Carl\] 
Of: (\[POLLARD:Carl\] \[HP:HP\] 
\[NATLANG:Natural Language Project\]) 
CONTINUING... 
U,+2: He manages Lyn. 
Cb: \[POLLARD:Carl\] 
CI: (\[POLLARD:A1\] \[FRIEDMAN:Lyn\]) 
He = Carl 
CONTINUING... Un+3: 
He promised to get her a raise. 
Cb: \[POLLARD:A1\] 
el: (\[POLLARD:A2\] \[FRIEDMAN:A3\] 
\[I~AISE:Xl\]) 
He = Carl, her = Lyn 
RETAINING... 
\[/,+4: She doesn't believe him. 
Cb: \[POLLARD:A2\] 
Cf: (\[FRIEDMAN:A4\] \[POLLARD:AS\]) 
She = Lyn, him = Carl 
Figure 2 
We are aware that this ranking usually coincides 
with surface constituent order in English. It would 
be of interest to examine data from languages with 
relatively freer constituent order (e.g. German) to de- 
termine the influence of constituent order upon cen- 
tering when the grammatical functions are held con- 
stant. In addition, languages that provide an identifi- 
able topic function (e.g. Japanese) suggest that topic 
takes precedence over subject. 
The part of the HPSG system that uses the cen- 
tering algorithm for pronoun binding is called the 
156 
pragmatics processor. It interacts with another mod- 
ule called the semantics processor, which computes 
representations of intrasentential anaphoric relations, 
(among other things). The semantics processor has 
access to information such as the surface syntactic 
structure of the utterance. It provides the pragmat- 
ics processor with representations which include of a 
set of reference markers. Each reference marker is 
contraindexed ~ with expressions with which it can- 
not co-specify 3. Reference markers also carry infor- 
mation about agreement and grammatical function. 
Each pronominal reference marker has a unique in- 
dex from Ax,...,An and is displayed in the figures 
in the form \[POLLARD:A1 L where POLLARD is 
the semantic representation of the co-specifier. For 
non-pronominal reference markers the surface string 
is used as the index. Indices for indefinites are gen- 
erated from XI,..., X,~. 
2 Extension 
The constraints proposed by \[GJW86\] fail in certain 
examples like the following (read with pronouns de- 
stressed): 
Brennan drives an Alfa Romeo. 
She drives too fast. 
Friedman races her on weekends. 
She often beats her. 
This example is characterized by its multiple am- 
biguous pronouns and by the fact that the final ut- 
terance achieves a shift (see figure 4). A shift is in- 
evitable because of constraint 3, which states that 
the Cb(U,~) must equal the Cp(U,-I) (since the 
Cp(Un-x) is directly realized by the subject of Un, 
"Friedman"). However the constraints and rules from 
\[GJW86\] would fail to make a choice here between the 
co-specification possibilities for the pronouns in U,. 
Given that the transition is a shift, there seem to be 
more and less coherent ways to shi~. Note that the 
three items being examined in order to characterize 
the transition between each pair of anchors 4 are the 
= See \[BP80\] and \[Cho80\] for conditions on coreference 
3 See \[Sid83\] for definition and discussion of co-specification. 
Note that this use of co-specification is not the saxne as that used in \[Se185\] 
4An anchor is a < Cb, Of > pair for an utterance 
Cb(U,,) = cpW.) 
Cb(V,,) # cp(u.) 
CbW.) = cb(~z._~) cbw.) # CbW,,_,) 
CONTINUING 
RETAINING 
SHIFTING-I 
SHIFTING 
Figure 3 : Extended Transition States 
Cb of U,,-1, the Cb of U,~, and the Cp of Un. By 
\[GJW86\] a shift occurs whenever successive Cb's are 
not the same. This definition of shifting does not 
consider whether the Cb of U, and the Cp of Un are 
equal. It seems that the status of the Cp of Un should 
be as important in this case as it is in determining 
the retaining/continuing distinction. 
Therefore, we propose the following extension 
which handles some additional cases containing mul- 
tiple ambiguous pronouns: we have extended rule 2 
so that there are two kinds of shifts. A transition 
for Un is ranked more highly if Cb(Un) = Cp(U,); 
this state we call shifting-1 and it represents a more 
coherent way to shift. The preferred ranking is 
continuing >- retaining >- shifting-1 ~ shifting (see 
figure 3). This extension enables us to successfully 
bind the "she" in the final utterance of the example 
in figure 4 to "Friedman." The appendix illustrates 
the application of the algorithm to figure 4. 
Kameyama \[Kam86\] has proposed another exten- 
sion to the \[G:JW86\] theory - a property-sharing con- 
straint which attempts to enforce a parallellism be- 
tween entities in successive utterances. She considers 
two properties: SUBJ and IDENT. With her exten- 
sion, subject pronouns prefer subject antecedents and 
non-subject pronouns prefer non-subject antecedents. 
However, structural parallelism is a consequence of 
our ordering the Cf list by grammatical function and 
the preference for continuing over retaining. Further- 
more, the constraints suggested in \[GJW86\] succeed 
in many cases without invoking an independent struc- 
tural parallelism constraint, due to the distinction 
between continuing and retaining, which Kameyama 
fails to consider. Her example which we reproduce in 
figure 5 can also be accounted for using the contin- 
157 
CONTINUING... 
U,,+I: Brennan drives an Alfa Romeo. 
Cb: \[BRENNAN:Brennan\] 
C f: (\[BRENNAN:Brennan\] \[X2:Alfa Komeo\]) 
CONTINUING... 
U,,+2: She drives too fast. 
Cb: \[BRENNAN:Brennan\] 
C f: (\[BRENNAN:AT\]) 
She = Brennan 
RETAINING... 
U,~+s: Friedman races her on weekends. 
Cb: \[BRENNAN:A7\] 
C f: (\[FRIEDMAN:Friedman\] \[BI~ENNAN:A8\] 
\[WEEKEND:X3\]) 
her = Brennan 
SHIFTING-l_. 
Un+4: She often beats her. 
Cb: \[FRIEDMAN:Friedman\] 
Of: (\[FRIEDMAN:A9\] \[BRENNAN:A10\]) 
She = Friedman, her = Brennan 
Figure 4 
CONTINUING... U,~+I: Who is Max waiting for? 
Cb: \[PLANCK:Max\] 
Of: (\[PLANCK:Max\]) 
CONTINUING... 
Un+2: He is waiting for Fred. 
Cb: \[PLANCK:Max\] 
C.f: (\[PLANCK:A1\] \[FLINTSTONE:Fred\]) 
He = Max 
CONTINUING... 
U,~+3: He invited him to dinner. 
Cb: \[PLANCK:A1\] 
of: (\[PLANCK:A2\] \[FLINTSTONE:A3\]) 
He - Max, him = Fred 
Figure 5 
uing/retaining distinction s. The third utterance in 
this example has two interpretations which are both 
consistent with the centering rules and constraints. 
Because of rule 2, the interpretation in figure 5 is 
preferred over the one in figure 6. 
3 Algorithm for centering and 
pronoun binding 
There are three basic phases to this algorithm. 
First the proposed anchors are constructed, then 
they are filtered, and finally, they are classified and 
ranked. The proposed anchors represent all the co- 
specification relationships available for this utterance. 
Each step is discussed and illustrated in figure 7. 
It would be possible to classify and rank the pro- 
posed anchors before filtering them without any other 
changes to the algorithm. In fact, using this strategy 
5It seems that property sharing of I'DENT is still necessary 
to account for logophoric use of pronouns in Japanese. 
CONTINUING... 
U,~+~: Who is Max waiting for? 
Cb: \[PLANCK:Max\] 
el: (\[PLANCK:Max\]) 
CONTINUING... 
U,~+2: He is waiting for Fred. 
Cb: \[PLANCK:Max\] 
el: (\[PLANCK:A1\] \[FLINTSTONE:Fred\]) 
he = Max 
RETAINING... Ur=+3: 
He invited him to dinner. 
Cb: \[PLANCK:A1\] 
el: (\[FLINTSTONE:A3\] \[PLANCK:A2\]) 
He = Fred, him = Max 
Figure 6 
158 
I. CONSTRUCT THE PROPOSED ANCHORS for Un 
(a) Create set of referring expressions (RE's). 
(b) Order KE's by grammatical relation. 
(c) Create set of possible forward center (C f) lists. Expand 
each element of (b) according to whether it is a pronoun 
or a proper name. Expand pronouns into set with entry 
for each discourse entity which matches its agreement 
features and expand proper nouns into a set with an 
entry for each possible referent. These expansions are 
a way of encoding a disjunction of possibilities. 
(d) Create list of possible backward centers (Cb's). This is 
taken as the entities f~om Cf(U,-1) plus an additional 
entry of NIL to allow the possibility that we will not 
find a Cb for the current utterance. 
(e) Create the proposed anchors. (Cb-O.f combinations 
from the cross-product of the previous two steps) 
2. FILTER THE PROPOSED ANCHORS 
For each anchor in our list of proposed anchors we apply the 
following three filters. If it passes each filter then it is still a 
possible anchor for the current utterance. 
(a) Filter by contraindices. That is, if we have proposed 
the same antecedent for two contraindexed pronouns 
or if we have proposed an antecedent for a pronoun 
which it is contraindexed with, eliminate this anchor 
from consideration. 
(b) Go through Cf(U,_,) keeping (in order) those which 
appear in the proposed Cf list of the anchor. If the 
proposed Cb of the anchor does not equal the first ele- 
ment of this constructed list then eliminate this anchor. 
This guarantees that the Cb will be the highest ranked 
element of the Cf(U,-t) realized in the current utter- 
ance. (This corresponds to constraint 3 given in section 
t) 
(c) If none of the entities realized as pronouns in the pro- 
posed C\[ list equals the proposed Cb then eliminate 
this anchor. This guarantees that if any element is re- 
alized as a pronoun then the Cb is realized as a pronoun. 
(If there are no pronouns in the proposed C\[ list then 
the anchor passes this filter. This corresponds' to rule 
1 in section 1). This rule could be implemented as a 
preference strategy rather than a strict filter. 
3. CLASSIFY and BANK 
EXAMPLE: She doesn't believe him. (U,+4 from figure 2) 
= (\[A4\] \[AS\]) 
=t, (\[A4\] \[AS\]) 
=~ (\[FRIEDMAN:A4\] \[POLLARD:A5\]) 
=> (\[POLLARD:A2\] \[FKIEDMAN:A3\] \[KAISE:XI\] NIL). 
=~ There are four possible < Cb, Cf > pairs for this utterance. 
i. <\[POLLARD:A2\], (\['FRIEDMAN:A4\] \[POLLARD:A5\])> 
ii. <\[FRIEDMAN:A3\], (\[FRIEDMAN:A4\] \[POLLARD:A5\])> 
iii. <\[KAISE:X1\], (\[FRIEDMAN:A4\] \[POLLARD:A$\])> 
iv. <NIL, (\[FRIEDMAN:A4\] \[POLLARD:A5\])> 
=~ This filter doesn't eliminate any of the proposed anchors in 
this example. Even though \[A4\] and \[A5\] are contraindexed 
we have not proposed the same co-specifier due to agreement. 
=~ This filter eliminates proposed anchors ii, iii, iv. 
=~ This filter doesn't eliminate any of the proposed anchors. 
The proposed Cb was realized as a pronoun. 
(a) Classify each anchor on the list of proposed anchors by =~ Anchor i is classified as a retention based on tim transition 
the transitions as described in section 1 taking U,~-t to state definition. 
be the previous utterance and U, to be the one we are 
currently working on. 
(b) Rank each proposed anchor using the extended rank- =~ Anchor i is the most highly ranked anchor (trivially). 
ing in section 2. Set Cb(Un) to the proposed Cb and 
Cf(Un) to proposed Cf of the most highly ranked an- 
chor. 
Figure 7 : Algorithm and Example 
159 
one could see if the highest ranked proposal passed all 
the filters, or if the next highest did, etc. The three 
filters in the filtering phase may be done in parallel. 
The example we use to illustrate the algorithm is in 
figure 2. 
4 Discussion 
4.1 Discussion of the algorithm 
The goal of the current algorithm design was concep- 
tual clarity rather than efficiency. The hope is that 
the structure provided will allow easy addition of fur- 
ther constraints and preferences. It would be simple 
to change the control structure of the algorithm so 
that it first proposed all the continuing or retaining 
anchors and then the shifting ones, thus avoiding a 
precomputation of all possible anchors. 
\[GJW86\] states that a realization may contribute 
more than one entity to the Cf(U). This is true 
in cases when a partially specified semantic descrip- 
tion is consistent with more than one interpreta- 
tion. There is no need to enumerate explicitly all 
the possible interpretations when constructing pos- 
sible C f(U)'s 6, as long as the associated semantic 
theory allows partially specified interpretations. This 
also holds for entities not directly realized in an ut- 
terance. On our view, after referring to "a house" 
in U,,, a reference to "the door" in U,~+I might be 
gotten via inference from the representation for '% 
house" in Cf(Un). Thus when the proposed anchors 
are constructed there is no possibility of having an 
infinite number of potential Cf's for an utterance of 
finite length. 
Another question is whether the preference order- 
ing of transitions in constraint 3 should always be 
the same. For some examples, particularly where 
U,~ contains a single pronoun and U,~-I is a reten- 
tion, some informants seem to have a preference for 
shifting, whereas the centering algorithm chooses a 
continuation (see figure 8). Many of our informants 
have no strong preference as to the co-specification 
of the unstressed "She" in Un+4. Speakers can avoid 
ambiguity by stressing a pronoun with respect to its 
phonological environment. A computational system 
6 Barbara Grosz, personal communication, and \[GJW86\] 
CONTINUING... 
Ur,+1: Brennan drives an Alfa P~omeo. 
Cb: \[BRENNAN:Brennan\] 
el: (\[BRENNAN:Brennan\] \[ALFA:X1\]) 
CONTINUING... 
U,~+2: She drives too fast. 
Cb: \[B1LENNAN:Brennan\] 
C f: (\[BRENNAN:A7\]) 
She - Brennan 
RETAINING... 
Un+3: Friedman races her on weekends. 
Cb: \[BB.ENNAN:A7\] 
C,f: (\[FRIEDMAN:Friedman\] 
\[BRENNAN:A8\]) 
\[WEEKEND:X3\]) 
her -- Brennan 
CONTINUING... 
U,~+4: She goes to Laguna Seca. 
Cb: \[BI~ENNAN:A8\] 
C f: (\[BRENNAN:A9\] \[LAG-SEC:Laguna Seca\]) 
She - Brennan?? 
Figure 8 
for understanding may need to explicitly acknowledge 
this ambiguity. 
A computational system for generation would try 
to plan a retention as a signal of an impending shift, 
so that after a retention, a shift would be preferred 
rather than a continuation. 
4.2 Future Research 
Of course the local approach described here does not 
provide all the necessary information for interpret- 
ing pronouns; constraints are also imposed by world 
knowledge, pragmatics, semantics and phonology. 
There are other interesting questions concerning 
the centering algorithm. How should the centering 
algorithm interact with an inferencing mechanism? 
Should it make choices when there is more than 
one proposed anchor with the same ranking? In a 
database query system, how should answers be in- 
160 
corporated into the discourse model? How does cen- 
tering interact with a treatment of definite/indefinite 
NP's and quantifiers? 
We are exploring ideas for these and other exten- 
sions to the centering approach for modeling reference 
in local discourse. 
5 Acknowledgements 
We would like to thank the following people for 
their help and insight: Hewlett Packard Lab's Natu- 
ral Language group, CSLI's DIA group, Candy Sid- 
net, Dan Flickinger, Mark Gawron, :John Nerbonne, 
Tom Wasow, Barry Arons, Martha Pollack, Aravind 
:Joshi, two anonymous referees, and especially Bar- 
bara Grosz. 
6 Appendix 
This illustrates the extension in the same detail as 
the example we used in the algorithm. The number- 
ing here corresponds to the numbered steps in the 
algorithm figure 7. The example is the last utterance 
from figure 4. 
EXAMPLE: She often beats her. 
I. CONSTRUCT THE PROPOSED AN- 
CHORS 
(a) (\[Ag\] \[A10\]) 
(b) (\[A9\] \[A10\]) 
(c) ((\[FRIEDMAN:A9\] \[FRIEDMAN:A10\]) 
(\[FRIEDMAN:A9\] \[BRENNAN:A10\]) 
(\[BRENNAN:A9\] \[BRENNAN:A10\]) 
(\[BRENNAN:A9\] \[FRIEDMAN:A10\])) 
(d) (\[FRIEDMAN:Friedman\] \[BRENNAN:A8\] 
\[WEEKEND:X3\] NIL) 
(e) There are 16 possible < Cb, Cf > pairs for 
this utterance. 
i. <\[FRIEDMAN:Friedman\], 
(\[FRIEDMAN:Ag\] \[FRIEDMAN:A10\])> 
ii. <\[FRIEDMAN:Friedman\], 
(\[FRIEDMAN:A9\] \[BRENNAN:A10\])> 
iii. <\[FRIEDMAN:Friedman\], 
(\[BRENNAN:A9\] \[FRIEDMAN:A10\]) > 
iv. < \[FRiEDMAN:Friedmaa\], 
(\[BRENNAN:A9\] \[BRENNAN:A10\])> 
v. <\[BRENNAN:A8\], 
(\[FRIEDMAN:Ag\] \[FRIEDMAN:A10\])> 
vi. <\[BRENNAN:A8\], 
(\[FRIEDMAN:Ag\] \[BRENNAN:A10\])> 
vii. <\[BRENNAN:A8\], 
(\[BRENNAN:A9\] \[FRIEDMAN:A10\])> 
viii. <\[BRENNAN:A8\], 
(\[BRENNAN:A9\] \[BRENNAN:A10\])> 
ix. <\[WEEKEND:X3\], 
(\[FRIEDMAN:Ag\] \[FRIEDMAN:A10\])> 
x. <\[WEEKEND:X3\], 
(\[FRIEDMAN:Ag\] \[BRENNAN:A10\])> 
xi. <\[WEEKEND:X3\], 
(\[BRENNAN:Ag\] \[FRIEDMAN:A10\])> 
xii. <\[WEEKEND:X3\], 
(\[BRENNAN:A9\] \[BRENNAN:A10\])> 
xiii. <NIL, 
(\[FRIEDMAN:Ag\] \[FRIEDMAN:A10\])> 
xiv. <NIL, 
(\[FRIEDMAN:A9\] \[BRENNAN:A10\])> 
xv. <NIL, 
(\[BRENNAN:Ag\] \[FRIEDMAN:A10\])> 
xvi. <NIL, 
(\[BRENNAN:A9\] \[BRENNAN:A10\])> 
2. FILTER THE PROPOSED ANCHORS 
(a) Filter by contraindices. Anchors i, iv, v, 
viii, iz, zii, ziii, zvi are eliminated since \[A9\] 
and \[A10\] are contraindexed. 
(b) Constraint 3 filter eliminates proposed an- 
chors vii, ix through zvi. 
(c) Rule 1 filter eliminates proposed anchors iz 
through zvi. 
3. CLASSIFY arid RANK 
(a) After filtering there are only two anchors 
left. 
ii: <\[FRIEDMAN:Friedman\], 
(\[FRIEDMAN:Ag\] \[BRENNAN:A10\])> 
iii: <\[FRIEDMAN:Friedman\], 
(\[BRENNAN:A9\] \[FRIEDMAN:A10\])> 
Anchor ii is classified as shifting-1 whereas 
anchor iii is classified as shifting. 
(b) Anchor ii is more highly ranked. 
161 
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162 
