STOCK OF SHARED KNOWLEDGE - 
- A TOOL FOR SOLVING PRONOMINAL ANAPHORA 
EVA HAJlt~OVA, VLADISLAV KUBOIq and PETR KUBOIq 
UFAL School of Computing Science 
Charles University Faculty of Applied Sciences 
Malostransk6 n~n. 25 Simon Fraser University 
CS-118 00 Prague Burnaby, B.C. V5A 1S6 
Czechoslovakia Canada 
ABSTRACT 
The paper develops further the idea of using the 
notion of the stock of shared knowledge (SSK) for 
anaphora resolution following a more subtle 
treatment of the influence of the topic/focus 
articulation of the sentence on the degrees of 
salience of items of the SSK. An algorithmic 
evaluation procedure of the SSK is formulated 
taking into account the notions of contextual 
boundness, syntactic associations, complexity of the 
sentences and existence/nonexistence of possible 
competitors, and a general evaluating function is 
proposed, essential for the process of anaphora 
resolution. In the present paper the analysis is 
performed for Czech; however, the considerations 
are claimed to be of a universal validity, the actual 
relations between different factors and the values, of 
course, being language-dependent. 
(MAX-l) than the items referred to in the focus 
part; 
(iii) a pronominal reference to an item in the 
topic part of the utterance keeps the activation 
unchanged; 
(iv) the items not mentioned in the given 
utterance subtract two degrees from their previous 
activation. 
As we stated already in the above mentioned 
paper, this was only a tentative solution on the way 
to a more sophisticated approach to organizing 
"common knowledge" of' the speaker and the 
hearer. 
Supported by a thorough linguistic analysis of 
a large amount of Czech prosaic texts Hoskovec 
(1989), two possible improvements to the procedure 
have been suggested Haji~ov~t, Hoskovec, Sgall (in 
press), namely: 
1 .INTRODUCTION 
In our paper at Coling'90 we followed up the 
investigations presented in HajEov~i, Vrbov~ (1982) 
and proposed an algorithm for solving pronominal 
anaphora with the use of "stock of shared 
knowledge" (SSK) - an abstract representation of 
the hierarchy of salience of the items of the 
knowledge assumed by the speaker to be shared by 
him and the hearer. The changes of degrees of 
salience were dependent solely on the bipolar 
division of the sentence into its topic and focus 
parts, respectively. In particular, the rules for 
computing the degrees of salience were specified as 
follows: 
(i) the items referred to in the focus part of the 
utterance be it by a noun or by a stressed pronoun 
receive the highest degree of salience (MAX); 
(ii) the items referred to by a noun in the topic 
part of the sentence are activated one degree less 
(i) to replace the binary account of topic/focus 
articulation of the sentence by a more atomic 
distinction between the contextually bound and 
non-bound elements of the sentence, thus enriching 
the numerical system of possible degrees of 
salience; 
(ii) to account explicitly for the empirical 
observations that items mentioned throughout the 
discourse are more likely to be referred to than 
those mentioned only once. 
In this paper we would like to argue that other 
important features should he taken into account in 
building the new evaluation system for the SSK. We 
believe that for a more sophisticated treatment of 
pronominal anaphora, an account of SSK must also 
allow for: 
(iii) a reflection of the topology of the surface 
structure of the text, in the simpliest form in terms 
of the distance of the possible antecedent and a 
ACRES DE COLING-92, NANTES, 23-28 Aofrr 1992 1 2 7 Pgoc. OF COLING-92, NANTES, AUO. 23-28, 1992 
refering expression measured by the number of 
interfering objects between them with respect to the 
sentence and paragraph boundaries; 
(iv) a capturing of some associations between 
lexieal units describing objects in the text. We have 
limited our attention only to syntactic associations 
between governing and dependent words in the 
syntactic structure of the sentence. More general 
treatment of associations requires the use of 
semantic and/or pragmatic information (eg. 
semantic features, knowledge base etc.) which is not 
taken into consideration in the present version of the 
algorithm, but forms a promising subject of further 
investigations of possible improvements of the 
algorithm. 
Taking these observations into account, we 
present a new, enriched model of SSK here. In 
Section 2 we briefly discuss the relevance of the 
above mentioned features for anapbora resolution. 
Section 3 gives a proposal of the organization of 
SSK, together with the rules evaluating changes in 
degrees of salience of its items and a general 
algorithm for reference assignment based on the use 
of SSK. The possibility of customizing the 
algorithm for the purposes of a special language 
under consideration (in our case Czech) is discussed 
in Section 4. 
2. MOTIVATION 
In our analysis, we work within the framework 
of the functional generative description (see Sgall, 
Haji~v~ and Panevovg, 1986). We represent the 
meaning structure of a sentence as a dependency 
tree rooted in the main verb, the nodes of the tree 
being labelled by lexical and morphological 
meanings. The edges denote the underlying 
grammatical relations between nodes. All nodes of 
the tree can be either contextually bound (CB) - if 
the objects they denote are "given', "known" from 
the context - or non-bound (NB) - if they introduce 
new information into discourse. 
The meaning of a sentence represented by such 
a tree is then viewed as divided into two parts - a 
topic (T), "stating" what the sentence is about, and 
a focus (F), commenting or developing the topic. 
The topic-focus articulation OVA) of a semence 
can be specified according to the sentence structure 
as follows (eL Sgall 1979): 
(i) F contains the main verb iff the verb is NB; 
(ii) F contains all daughter nodes of the verb 
which are N'B, together with all nodes subordinated 
to them (which in tam are either NB or CB); 
(iii) if the verb together with all daughter nodes 
is CB (and, therefore, none of (i),(ii) applies), F is 
defined with respect to a deeper embedded node. 
( This ease is rather rare and we do not consider it 
in our analysis for the sake of simplicity.) 
(iv) T consists of all the nodes not contained in 
F. 
Thus, for the purpose of this paper, only the 
difference between NB nodes and CB nodes on the 
first level of dependency is taken into consideration 
while specifying TFA of a sentence. We would like 
to show in the sequel that there is a linguistic 
evidence which suggests .that deeper levels of 
syntactic embedding (at least the second level) be 
accounted for in the resolution of anaphora. 
For the sake of simplicity, we represent the 
sentence schematically: 
Verb 
I i I 
o(1) G(2) 
i i 1 I I I 
G(3) G(4) G(5) G(6) 
Topic Focus 
where G(I) is a group of CB nodes on the first level 
of dependency (belonging to T), 
G(3), G(5) are CB nodes on the second level 
of dependency (belonging to T and F respectively), 
G(2) is a group of NB nodes on the first level 
of dependency (belonging to F) and 
G(4), G(6) are NB nodes on the second level 
of dependency (belonging te T and F respectively). 
2,1 Level of dependency in a syntactic tree 
Let us introduce one of the examples which 
show the necessity of further extension of the scale 
in the SSK. Consider the following sample of text 
- Hoskovee (1989): 
Ex.l: 
(1) At the railway station I saw a dog with long 
ears. 
(2) It was funny to observe them dangling in the 
wind. 
(3) I wondered how he happened to get there. 
According to our Coling '90 paper there is no 
distinction in the SSK between dog and ears. Both 
are contained in focus of (1), which means that they 
have the highest degree of salience in the next 
sentence. Such an account does not explain the fact, 
that the above introduced order of sentences is 
ACIT.S DE COLING-92, N^NrEs, 23-28 ^O't~T 1992 l 2 8 PROC. OF COLING-92, NANTES, AUG. 23-28, 1992 
possible and the order (1)-(3)-(2) does not constitute 
a coherent text - it seems to be impossible to refer 
to ears from the third sentence using the personal 
pronoun them as a refering expression. 
The scheme of the syntactic tree as introduced 
above offers us a key to the solution of this 
problem. From this point of view there is a 
distinction between dog and ears in the sentence 
(1). According to our scheme, the word dog stands 
in the position G(2), the word ears is in the position 
G(6). Both are contextually non-bound. 
Thus, examples along this line seem to suggest 
that the modified SSK has to take into account the 
distinction between immediate members of a 
respective verb frame and words which are 
embedded on a deeper level of the syntactic tree. 
2.2 Contextual boundness and non-boundness 
The distinction between contextually bound and 
non-bound elements is also significant. Let us 
consider the following example from Hoskovec 
(1989): 
Ex.2: 
(4) At the railway station I saw their dog. 
(5) I realized they would look for him the whole 
afternoon. 
(6) I wondered how he happened to get there. 
Although this sample text seems to have the 
same distribution of pronouns as (1)-(3), the 
difference between the two texts shows when we 
change the order of sentences to (4)-(6)-(5). In the 
latter case, the change of the order is possible . 
Since the sentences (1) and (4) differ only in 
contextual non-boundness of long ears vs. 
contextual boundness of their, respectively, both 
expressions being on the second level of 
dependency, we conclude that the distinction 
between contextual boundness/nonboundness of the 
nodes in the syntactic tree of the sentence is 
important for the resolution of anaphora and, 
therefore, must be captured by the new version of 
SSK. 
2.3 Syntactic associations 
The notion of syntactic associations is 
introduced by means of slightly modified examples 
found in technical texts. Let us start with the 
following sample text: 
Ex.3: 
(7) In the residence quarter of Brno it is possible to 
find a villa of professor Sehmidt. 
(8) It was built during the thirties. 
(9) His other two hgat,S~ are to be found in 
Olomouc and Jihlava. 
In this case the assignment of him to its 
antecedent is straightforward; although the 
expression professor SchmMt is in the focus part of 
(7), it does not depend directly on the governing 
verb and, moreover, it is contextually non-bound. 
At the first sight this seems to be a counterexample 
to the above introduced scheme of the role of CB 
and NB elements of a sentence, namely, to the 
impossibility of referring to NB-nodes on the 
second level of dependency by means of personal 
pronouns across one embedded sentence (see Ex. 1). 
However, we believe that the difference 
between (1)-(2)-(3) and (7)-(8)-(9) lies in the fact, 
that his is in the third sentence accompanied by the 
full noun reference to the v_~iLusing a similar word 
(house), which certainly influences the salience of 
the item professor Schmidt. The structure of a noun 
phrase governed by villa in (7) is the same as the 
dependency structure of the noun phrase governed 
by ~ in (9), therefore also the salience of the 
item professor Schmidt is evidently higher than 
without that association. We can support our 
observations with the modified example: 
Ex.4: 
(7) In the residence quarter of Brno it is possible to 
find a v_illa of professor Schrnidt. 
(8) It was built during the thirties. 
(9a) He was known as a collector of paintings of 
young local painters. 
In our opinion, the process of assigning the 
antecedent professor Schmidt to the refering 
expression him is not as straightforward as in Ex.3; 
indeed, some of the hearers have difficulties with 
accepting Ex.4 as a valid tgxt. 
The degree of the influence of syntactic 
associations on anaphora resolution can vary for 
different languages. It is also clear that at least a 
sm'all stock of related notions plays a very important 
role in this mechanism. We will discuss these 
problems more in detail in the Sect. 4 of this paper, 
where we show the approach for a particular 
language under consideration (Czech). 
2.4 Topology 
We can use Ex.3 to show another important fact 
which has an influence on the reference assignment. 
The sentence (8) is a very simple one, in particular, 
it does not introduce any new element into the SSK 
except the word thirties. The situation is very 
different, if we replace (8) by (Sa): 
AcrEs DE COLING-92, NANTES, 23-28 hOlY 1992 1 2 9 PROC, OV COLINGO2. NANTES, AUG. 23-28, 1992 
Ex.5: 
(7) In the residence quarter of Brno it is possible to 
find a villa of professor Schmidt. 
(Sa) The buildin~ was built by a group of architects 
in late thirties. 
(9) His other two ~ are to be found in 
Olomouc and Jihlava. 
The reference by him in (9) is in this case still 
possible, but the text is not as clear as in Ex.3. Any 
other new element in (Sa) makes the reference 
almost unclear. 
Supported by this observation, we believe that 
also the linear distance between an antecedent and 
a refering expression influences to some extent the 
salience of the referred item. 
It is clear that the function which expresses the 
degree of salience is not continuous. The end of the 
paragraph seems to have a strong effect: it leads to 
a drop of the salience of almost all possible 
antecedents except tbr those the activation of which 
has been established by repeated mentioning in the 
previous paragraph. The exact values of the 
function are now the objects of intensive 
investigation. We discuss some results of our 
investigations into this problem in Sect. 4 below. 
2.5 Existence of competitors 
The last feature which is considered in our 
system is the role of competing elements. We can 
demonstrate the problem by means of a slight 
change of (8a), which introduces a new competing 
element into the text: 
Ex.6: 
(7) In the residence quarter of Brno it is possible to 
find a ~ of professor Schmidt. 
(Sb) The building was built by architect Hovorka in 
late thirties. 
(9) His other two ~ are to be found in 
Olomouc and Jihlava. 
In this case professor Schmidt is no longer 
available as an antecedent for pronominal anaphora 
Since architect Hovorka has a greater degree of 
salience and the same morphological categories. 
All previous examples show the necessity of 
including into the evaluation procedure of the SSK 
not only the notions of contextual boundness, but 
also associations, complexity of the sentences and 
existence/nonexistence of possible competitors. 
Their role in the evaluation procedure is described 
more in detail in the following paragraph. 
3. THE GENERAL EVALUATING PROCEDURE 
Before we start the explanation of our 
evaluation procedure, we must make clear that we 
restrict ourselves in our considerations to those 
items of knowledge (i.e.the mental representations 
of the objects of the outer world), referred to in the 
sentence by noun or by a pronoun. The starting 
conditions for the evaluating procedure are then as 
follows: 
We assume that our procedure is a part of a 
larger complex system which is able to provide our 
procedure with the result of syntactico-semantic 
parsing of any sentence in the form of a dependency 
tree as a representation of the meaning of the 
sentence in the sense of Sgall, Haji~ov~l, Panevov~, 
(1986). We do not assume the existence of any 
special knowledge base, any semantic evaluation 
procedure or semantic features present in the 
syntactic tree. For the time beeing we restrict 
ourselves to those items (mental objects) that are 
rendered by nouns or pronouns. 
The SSK as a basic data structure can be viewed 
in our modified account as a set of items, which 
represent all mental objects rendered by nouns or 
pronouns from the respective text. Each data entry 
has the form of an ordered quantuple: 
< LEX,MORPH,LAST,SYNT,OCCUR >, 
where 
LEX 
represents the lexical value of the item; 
MORPH 
is a set of morphological characteristics of 
the word (e.g. gender, number, etc.). These 
characteristics are used in so-called morphological 
filter, which filters out the impossible antecedents of 
the referring expression. 
LAST 
are the coordinates 'of the latest occurrence 
of the word or of the pronominal reference to it. 
These coordinates are composed of the "surface" 
and "deep" part. The "surface" coordinates contain 
the number of the sentence and a serial number of 
the node in the sentence structure and they serve as 
a basis for the "topological" part of the evaluation 
procedure. 
The "deep" part contains the code for the 
position of the word in the syntactic tree as 
introduced above (G(i)). This information 
determines the contextual (non)boundness of the 
word. 
ACRES DE COL/NG-92. NAM'ES, 23-28 Ao(rr 1992 ! 3 0 PROC. OF COLING-92, NANTES, AUG. 23-28. 1992 
SYNT 
contains the data about the syntactic 
structure of the sentence where the respective LEX 
was mentioned for the last time. The structure is 
represented only partially, by means of pointers, 
which point to the governing node and also to all 
dependent nodes if they are contained in the SSK. 
This system of syntactic pointers serves as a basic 
data structure for the simple handling of 
associations. 
OCCUR 
is a pair of integers which represent the 
number of occurrences of the given item both from 
the beginning of the text and from the beginning of 
the paragraph. 
The algorithm processes the given text sentence 
by sentence. It receives the dependency tree of a 
new sentence from the syntactico-semantical 
preprocessor, together with the list of all the 
pronominal referring expressions contained in the 
sentence. Each referring expression in this list 
carries the information about its position in the 
sentence (the same as LAST) and about its form 
(weak or strong pronoun, etc.). Using SSK, the 
algorithm finds the antecedents for all referring 
expressions. Afterwards, it changes the degrees of 
salience of the items in the SSK and reads the next 
sentence from the input. 
Having stated the general idea of the algorithm, 
we can describe the evaluation process in more 
detail as follows: 
3.1 Algorithm: 
(i) Read an input (the syntactic structure of the new 
sentence and the list of referring expressions). For 
every referring expression R~, i= 1 ,..,k in the list do 
the following (preserve the order of the referring 
expressions with regard to hierarchy of 
communicative dynamism in case that the sentence 
contains more than one referring expression): 
a) Use the morphemic filter to filter out all units 
from the SSK which cannot be considered as 
possible antecedents of the refering expression 1~. 
b) Apply the evaluating function E(w) to all 
possible ~ 2 k 
antecedents Wi,Wi,...,Wi i and sort them according 
to the obtained results from the most probable 
antecedent W~i i to the least probable antecedent W~. i. 
(ii) For all referring expressions P~ and all results of 
J evaluation W~, i=l,..,k; j=l,..,l~ find the best 
solution. 'Ihus we are lot~king for the optimal 
solution of anapbora for the sentence as a whole, 
since some "best" solution tbr the particular 
expression can block successful reference 
assignment for other refering expressions (Cf. 
examples in Haji~ovg, Kubo/'t, Kubofi, 1990). 
Generally, this is a computationally expensive 
solution but ill practice the nnmber of refering 
expression and possible antecedents is strongly 
limited and, therefore, this phase does not impose 
a serious restriction on the performance of the 
algorithm. 
(iii) Update the data in the SSK 
- change OCCUR if the item was mentioned or 
reffered to in the current sentence 
- add items mentioned for the first time into the 
SSK 
- remove all the items with degrees of salience 
function smaller than some constant THRESHOLD 
(which may vary with respect to the 
type of the text and the particular language). 
The function of salience has the form: 
p 
S(w) = O/(N*(N-L + 1)), 
where w is the item of SSK under consideration, 
O is the number of occurrences of the item in the 
given paragraph 
N is the serial number of the current utterance (in 
the given paragraph) 
L is the serial number of the utterance in the 
paragraph where this item was mentioned 
for the last time 
3.2 The general evaluating function 
This function is essential for the whole process 
of anaphora resolution. Also, it is considerably 
more dependent on the language under consideration 
than all the other parts of the process. For this 
reason we have divided its description into two 
parts, in this section we describe the function only 
generally. The method of costomizing all the 
constants according to the needs of a particular 
language (in our case Czech) is described in Sect. 
4 below. 
The basic form of the function is: 
E(W) = ~ (ci* f~}, 
i-I 
where f~ is a function describing the value of the 
factoq 
q is a constant expressing file weight of the 
factoq 
ACRES DE COLING-92. NAN,S, 23-28 AO't3"r 1992 1 3 1 Prtoc. or COLING-92. NANI'ES. AUG. 23-28. 1992 
4. THE METHOD OF THE CUSTOMIZATION 
OF THE EVALUATING FUNCTION 
In this paragraph we want to show the 
method chosen for finding the values of the ~, and f~ 
for the particular language. 
4.1 First step of the method is to find the form of 
f~ for all factors taken into account. All functions 
should have a common value range. The balance of 
influence of all factors is achieved by the help of 
constants % After a complex examination of Czech 
texts (with a special stress on technical texts) we 
have come to the following results: 
a) Contextual boundness - the word w is either 
bound or nonbound, therefore 
ft(w) = 100 < = > w is contextually 
bound 
f~(w) = 0 < => w is contextually 
nonbound 
b) Underlying structure - for the definition of this 
function it is necessary to extend our schema from 
the paragraph 2 deeper than to the second level of 
dependency. The rule for the extension is the 
following: 
All deeper levels consist only of nodes 
belonging to groups G(3-6) so that any governing 
node in the topic governs nodes GO) and G(4), the 
governing nodes from focus govern nodes G(5) and 
G(6). 
The function f:has been assigned the following 
(entative forms: 
f2(w) = 70 for w in a position of G(1) 
f2(w) = 100 for w in a position of G(2) 
f2(w) = 50 for w in a position of G(3) 
f2(w) = 0 for w in a position of G(4) 
f2(w) = 50 for w in a position of G(5) 
f2(w) = 30 for w in a position of G(6) 
The motivation for this distribution of values 
can be found in Hoskovec (1989). 
c) Associations - if the word wl depends directly on 
the word w:, it shares a part of the value of E(wz). 
We do not restrict the dependency only to the 
immediate dominance, but the words on a deeper 
level share less of the value E(wz). We also take 
into account that one word can be in principle 
associated with more than one other member of the 
SSK. Therefore the form of the function f3 is the 
following: 
wt .... w, are the governing words of w so that 
wi are ordered according to the syntactic level (w, is 
the immediate governor of w) 
f3(w) = ~ (1/2 i) * E(w.) 
i-1 
d) Linear distance - this function is quite simple, it 
is only necessary to count the linear distance of w 
and the possible refering expression. The counting 
is easy - we count only the members of SSK. 
The function is simple:, 
f4(w) = 100/((In d)+d) 
where d is a distance between the word w and 
a possible refering expression. 
4.2 There is of course a significant difference 
between the way of computing f~ and % The latter 
is a constant, which describes the role of particular 
factors in the respective language. 
For the evaluation of weights cl we use the 
following method: 
In real texts we look for pieces of text with 
complicated referring structure. Any such text is 
modified by adding or removing items. The results 
are given to a group of randomly chosen native 
speakers, who should mark the understandability of 
all texts. One example of this method is given here 
by the modification of sentences (8) and (9) in Ex. 
4 and 5 above. 
The basic constraint on ¢, is described in the 
following equation: 
~cl = 1 
i-I 
which means that every c~ describes the role of 
factor i in percents. This constraint serves for the 
purpose of keeping the balance between particular 
factors under control. It is also useful in the case of 
some future extension of the whole system by 
adding new factors. 
There can of course be any other constraints 
according to the needs of a particular language. We 
do not have any additional constraint for Czech in 
the moment. 
The work on collecting material for the tests on 
c~ is now in progress. The following constants were 
chosen as initial values : 
- contextual boundness and non-boundness 0.25 
- syntactic structure of the sentence 0.25 
- associations 0.25 
- linear distance 0.25 
5. CONCLUSION 
The previous analysis has been done for Czech. 
We are far from claiming that every language would 
ACT~ DE COLING-92, NANTES, 23-28 AOU'r 1992 I 3 2 PROC. OF COLING-92, NANTES, AUG. 23-28, 1992 
reflect the same relations between factors which can 
help to solve the pronominal anaphora. We have 
only tried to show how certain factors can result in 
a more sophisticated treatment of anaphora in NLP. 
Our mechanism is designed as an open system, 
the nature of all functions mentioned here enables to 
add any number of other phenomena which can help 
to solve the problem of anaphora re.solution. 
Our approach is substantially different from the 
approach of e.g. Alshawi (1987) or Rich, 
Luper-Foy (1987). Our system does not need any 
knowledge base except the special thesaurus of 
related notions. It would be very interesting to 
combine our approach and the approach of Alshawi 
in some experimental NLP system. 
ACKNOWLEDGEMENT 
the work on this paper was carried out 
under the project of the IBM Academic Initiative. 
AfrlT.:s DE COL1NG-92. NANTES. 23-28 AOt'rl' 1992 1 3 3 t)ROC. OF COLlNG-92. NANTES. AUG. 23-28. 1992 

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