Towards resolution of bridging descriptions 
Renata Vieira and Simone Teufel 
Centre for Cognitive Science - University of Edinburgh 
2, Buccleuch Place EH8 9LW Edinburgh UK 
{renat a, simone}©cogsci, ed. ac. uk 
Abstract 
We present preliminary results concern- 
ing robust techniques for resolving bridging 
definite descriptions. We report our anal- 
ysis of a collection of 20 Wall Street Jour- 
nal articles from the Penn Treebank Cor- 
pus and our experiments with WordNet to 
identify relations between bridging descrip- 
tions and their antecedents. 
1 Background 
As part of our research on definite description (DD) 
interpretation, we asked 3 subjects to classify the 
uses of DDs in a corpus using a taxonomy related 
to the proposals of (Hawkins, 1978) (Prince, 1981) 
and (Prince, 1992). Of the 1040 DDs in our corpus, 
312 (30%) were identified as anaphoric (same head), 
492 (47%) as larger situation/unfamiliar (Prince's 
discourse new), and 204 (20%) as bridging refer- 
ences, defined as uses of DDs whose antecedents-- 
coreferential or not--have a different head noun; the 
remaining were classified as idioms or were cases for 
which the subjects expressed doubt--see (Poesio and 
Vieira, 1997) for a description of the experiments. 
In previous work we implemented a system ca- 
pable of interpreting DDs in a parsed corpus 
(Vieira and Poesio, 1997). Our implementation 
employed fairly simple techniques; we concentrated 
on anaphoric (same head) descriptions (resolved by 
matching the head nouns of DDs with those of 
their antecedents) and larger situation/unfamiliar 
descriptions (identified by certain syntactic struc- 
tures, as suggested in (Hawkins, 1978)). In this 
paper we describe our subsequent work on bridging 
DDs, which involve more complex forms of common- 
sense reasoning. 
2 Bridging descriptions: a corpus 
study 
Linguistic and computational theories of bridg- 
ing references acknowledge two main problems in 
their resolution: first, to find their antecedents 
(ANCHORS) and second, to find the relations (LINKS) 
holding between the descriptions and their anchors 
(Clark, 1977; Sidner, 1979; Heim, 1982; Carter, 
1987; Fraurud, 1990; Chinchor and Sundheim, 1995; 
Strand, 1997). A speaker is licensed in using a bridg- 
ing DD when he/she can assume that the common- 
sense knowledge required to identify the relation is 
shared by the listener (Hawkins, 1978; Clark and 
Marshall, 1981; Prince, 1981). This reliance on 
shared knowledge means that, in general, a system 
could only resolve bridging references when supplied 
with an adequate lexicon; the best results have been 
obtained by restricting the domain and feeding the 
system with specific knowledge (Carter, 1987). We 
used the publicly available lexical database Word- 
Net (WN) (Miller, 1993) as an approximation of a 
knowledge basis containing generic information. 
Bridging DDs and WordNet As a first experi- 
ment, we used WN to automatically find the anchor 
of a bridging DD, among the NPs contained in the 
previous five sentences. The system reports a se- 
mantic link between the DD and the NP if one of 
the following is true: 
• The NP and the DD are synonyms of each other, 
as in the suit -- the lawsuit. 
• The NP and the DD are in direct hyponymy 
relation with each other, for instance, dollar -- the 
currency. 
• There is a direct or indirect meronymy (part- 
of relation) between the NP and the DD. Indirect 
meronymy holds when a concept inherits parts from 
its hypernyms, like car inherits the part wheel from 
its hypernym wheeled_vehicle. 
• Due to WN's idiosyncratic encoding, it is often 
522 
necessary to look for a semantic relation between 
sisters, i.e. hyponyms of the same hypernym, such 
as home -- the house. 
An automatic search for a semantic relation in 
5481 possible anchor/DD pairs (relative to 204 
bridging DDs) found a total of 240 relations, dis- 
tributed over 107 cases of DDs. There were 54 cor- 
rect resolutions (distributed over 34 DDs) and 186 
false positives. 
Types of bridging definite descriptions A 
closer analysis revealed one reason for the poor 
results: anchors and descriptions are often linked 
by other means than direct lexico-semantic rela- 
tions. According to different anchor/link types and 
their processing requirements, we observed six ma- 
jor classes of bridging DDs in our corpus: 
Synonymy/Hyponymy/Meronymy These DDs 
are in a semantic relation with their anchors that 
might be encoded in WN. Examples are: a) Syn- 
onymy: new album -- the record, three bills -- 
the legislation; b) Hypernymy-Hyponymy: rice -- 
the plant, the television show -- the program; c) 
Meronymy: plants -- the pollen, the house -- the 
chimney. 
Names Definite descriptions may be anchored to 
proper names, as in: Mrs. Park -- the housewife 
and Pinkerton's Inc -- the company. 
Events There are cases where the anchor of a bridg- 
ing DD is not an NP but a VP or a sentence. Ex- 
amples are: ...individual investors contend. -- They 
make the argument in letters...; Kadane Oil Co. is 
currently drilling two wells... -- The activity ... 
Compound Nouns This class of DDs requires con- 
sidering not only the head nouns of a DD and its 
anchor for its resolution but also the premodifiers. 
Examples include: stock market crash -- the mar- 
kets, and discount packages -- the discounts. 
Discourse Topic There are some cases of DDs 
which are anchored to an implicit discourse topic 
rather than to some specific NP or VP. For instance, 
the industry (the topic being oil companies) and the 
first half (the topic being a concert). 
Inference One other class of bridging DDs includes 
cases based on a relation of reason, cause, conse- 
quence, or set-members between an anchor (previous 
NP) and the DD (as in Republicans/Democratics -- 
the two sides, and last week's earthquake -- the suf- 
fering people are going through). 
The relative importance of these classes in our 
corpus is shown in Table 1. These results explain 
in part the poor results obtained in our first experi- 
ment: only 19% of the cases of bridging DDs fall into 
the category which we might expect WN to handle. 
Class # % Class # % 
S/H/M 38 19% C.Nouns 25 12% 
Names 49 24% D.Topic 15 07% 
Events 40 20% Inference 37 18% 
Table 1: Distribution of types of bridging DDs 
3 Other experiments with WordNet 
Cases that WN could handle Next, we consid- 
ered only the 38 cases of syn/hyp/mer relations and 
tested whether WN encoded a semantic relation be- 
tween them and their (manually identified) anchors. 
The results for these 38 DDs are summarized in Ta- 
ble 2. Overall recall was 39% (15/38). 1 
Class Total Found in WN Not Found 
Syn 12 4 8 
Hyp 14 8 6 
Mer 12 3 9 
Table 2: Search for semantic relations in WN 
Problems with WordNet Some of the missing 
relations are due to the unexpected way in which 
knowledge is organized in WN. For example, our 
artifact 
I structure/1 
construction/4 
. part of 
housing building ~ 
lodging edifice " all /\ 
house dwelling, home /~ 
part_of 
specific houses blood family 
Figure 1: Part of WN's semantic net for buildings 
method could not find an association between house 
and walls, because house was not entered as a hy- 
ponym of building but of housing, and housing does 
1 Our previous experiment found correct relations for 
34 DDs, from which only 18 were in the syn/hyp/mer 
class. Among these 18, 8 were based on different anchors 
from the ones we identified manually (for instance, we 
identified pound -- the currency, whereas our automatic 
search found sterling -- the currency). Other 16 correct 
relations resulting from the automatic search were found 
for DDs which we have ascribed manually to other classes 
than syn/hyp/mer, for instance, a relation was found for 
the pair Bach -- the composer, in which the anchor is 
a name. Also, whereas we identified the pair Koreans 
-- the population, the search found a WN relation for 
nation -- the population. 
523 
not have a meronymy link to wall whereas building 
does. On the other hand, specific houses (school- 
house, smoke house, tavern) were encoded in WN 
as hyponyms of building rather than hyponyms of 
house (Fig. 1). 
Discourse structure Another problem found in 
our first test with WN was the large number of false 
positives. Ideally, we should have a mechanism for 
focus tracking to reduce the number of false posi- 
tives- (Sidner. 1979), (Grosz, 1977). We repeated 
our first experiment using a simpler heuristic: con- 
sidering only the closest anchor found in a five sen- 
tence window (instead of all possible anchors). By 
adopting this heuristic we found the correct anchors 
for 30 DDs (instead of 34) and reduced the number 
of false positives from 186 to 77. 
4 Future work 
We are currently working on a revised version of the 
system that takes the problems just discussed into 
account. A few names are available in WN, such as 
famous people, countries, cities and languages. For 
other names, if we can infer their entity type we 
could resolve them using WN. Entity types can be 
identified by complements like Mr., Co., Inc. etc. 
An initial implementation of this idea resulted in 
the resolution of .53% (26/49) of the cases based 
on names. Some relations are not found in WN, 
for instance, Mr. Morishita (type person)-- the 57 
year-old. To process DDs based on events we could 
try first to transform verbs into their nominalisa- 
tions, and then looking for a relation between nouns 
in a semantic net. Some rule based heuristics or a 
stochastic method are required to 'guess' the form 
of a nominalisation. We propose to use WN's mor- 
phology component as a stemmer, and to augment 
the verbal stems with the most common suffixes for 
nominalisations, like -ment, -ion. In our corpus, 16% 
(7/43) of the cases based on events are direct nom- 
inalisations (for instance, changes were proposed -- 
the proposals), and another 16% were based on se- 
mantic relations holding between nouns and verbs 
(such as borrou~,ed -- the loan). The other 29 cases 
(68%) of DDs based on events require inference rea- 
soning based on the compositional meaning of the 
phrases (as in It u~ent looking for a partner -- the 
prospect); these cases are out of reach just now, as 
well as the cases listed under "'discourse topic" and 
"inference". We still have to look in more detail at 
compound nouns. 

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