e 
Parsing into Discourse Object Descriptions 
Lars Ahrenberg 
Department of Computer and Information Science 
Link6ping University 
S - 581 83 Link~ping 
ABSTRACT 
This paper reports work on the design of a natural 
language interface with a limited dialogue capability. It is 
argued that (i) The interpretation of the input is 
preferably represented as a structure of Discourse Object 
Descriptions (DODs); (ii) The DODs must be determined 
on the basis of different types of knowledge such as 
grammatical knowledge, object type deirmitions and 
knowledge about existing discourse objects and their 
discourse status; (iii) The different types of knowledge are 
stored separately but integrated in the interpretation 
process which is based on constraints. 
INTRODUCTION 
The LINLIN-project is concerned with the development 
of general-purpose natural language interfaces (NLIs) to 
computer software with special emphasis on 
communication in Swedish. A useful general-purpose NLI 
must meet a var/ety of requirements, a number of which 
concern communicative ability. The communicative 
abilities of as NLI are necessarily restricted by the 
limitations of existing techniques, but can also be 
purposely restricted to enhance transparency. It is not 
certain that the linguistically more competent NLI is the 
most useful one, e.g. if its behaviour appears idiosyncratic 
to the user. In any case, the language of an NLI is a 
language designed (and is in that respect not a natural 
language) so there are many questions to be answered 
about how it should be designed, both in terms of how it 
should function as a vehicle of communication and in terms 
of internal representations and procedures. 
As for the first aspect we are conducting a series of 
simulations to fred out what communicative abilities an 
NLI should have (Dahlb~k&J~neson, 1986), but 
meanwhile we are assuming that LINLIN should meet the 
following demands: it should have a fair knowledge of the 
structure of Swedish words, clauses and utterances, an 
s 
This work is part of the project Analysis and Generation of 
Natural-Lan~,ruage Texts supported by the National Swedish 
Board of Technical Development. 
extendable lexicon, an extendable knowledge of object 
types, an ability to cope with directives, questions and 
assertions as they relate to the current background 
system(s) and a restricted ability to engage in a dialogue 
with the user. 
The dialogue capabilities of LINLIN are primarily 
designed for the following purposes: (a) to enable the user 
to make explicit and implicit cross-references between 
utterances, e.g. by using pronouns and ellipsis; (b) to allow 
the user to build commands incrementally; (c) to ask the 
user for clarifications and other information that the 
system might need, and (d) to provide help for the user. 
In this paper some consequences of these demands for 
the representation and interaction of various types of 
knowledge that the system needs are considered. The main 
ideas are t~e following: (1) The content of user inputs is 
preferably represented as a structure of Discourse Object 
Descriptions (DODs) which relate in various ways to 
objects of the universe of discourse. (2) Different types of 
knowledge, including object type knowledge and knowledge 
of the current state of the discourse must be used and 
integrated in the construction of an interpretation. (3) To 
ensure generality and in contrast to the entity-oriented 
parser of Hayes (1984), the grammatical knowledge is not 
exclusively tied to object type definitions but stored 
separately. (4) Knowledge about the discourse status of 
objects is also a kind of general knowledge that must be 
kept separate from object type definitions. (5) In a 
constraint-based parsing process the grammatical 
descriptions and the content descriptions can be built in 
tandem, sometimes with the syntax in control and 
sometimes with the object knowledge in control. This 
allows us to diminish the role of the syntactic part of the 
parsing to recognition of significant structural patterns, 
using semantic and pragmatic knowledge for the resolution 
of structural ambiguities such as PP-attachment. 
The first background system that LINLIN will work on 
is a group calendar. As the pilot Version of LINLIN is only 
in its initial stages my arguments will mainly be 
theoretical, while the practicality of the proposed ideas 
remains to be proven. 
140 
TH\]~ FRA.M~WORK 
Discourse Objects 
Virtually anything that can be perceived as and talked 
about as an individual may serve as a discourse object. 
Thus, objects and facts represented in a database as well 
as the user's inputs, the commands to be executed and the 
responses of the system are all (potential) discourse 
objects. Notions such as discourse elements (Sidner, 1984) 
and discourse entities (Webber, 1984) have been employed 
to denote the entities that are =specified" or evoked by the 
constituents of a discourse, they and their relations then 
constituting the discourse model of a speaker. Hayes (1984) 
refers to the objects, events, commands, states (and so on) 
that an interface system needs to recognize collectively as 
"entitities ~. In the same vein I ta~e the notion of a 
discourse object to apply in the most general sense; the 
universe of discourse is in principle just a collection of 
discourse objects. A relation between discourse objects is 
also a discourse object although it may also, or 
alternatively, be attributed to one or more of its 
constituents as part of their descriptions. 
All discourse objects are instances of one or more object 
types. Thus, we allow a discourse object to be viewed from 
complementary perspectives. For instance, from a 
grammatical perspective an input may be typed as a 
declarative sentence, whereas from an interactional 
perspective it may be typed as an answer and both of these 
categorizations may contribute information about its 
content. 
Discourse Object Descriptions 
The information that the system has of a particular 
discourse object is encoded in a discourse object 
description, or DOD, for short. As discourse objects 
generally will have some information attached to them, we 
may represent a discourse object as a pair of s unique label 
and a DOD. 
DODs have the format of structures of attribute-value 
pairs where the attributes represent informational 
dimensions, i.e. ways of predicating something of the 
object, and the values encode whatever information is 
available for that dimension. An attribute of special 
importance is Instance-Of which relates a discourse 
object to a type. Other attributes are generally inherited 
from an object type definition which occurs as part of the 
description of an object type. An object type definition can 
be viewed as a skeleton for a typical instance of that type 
registering the defining attributes as well as restrictions on 
their values. For events, such as meetings or bookings, the 
object type definition is basically similar to a ca~e frame 
(see figure 1). The object type definitions thus encode the 
system's semantic knowledge, whereas the universe of 
discourse encodes its world knowledge. 
B 
Label: 'Meeting 
Typical-instance: 
ieeting-type: ~sa 'Meeting\] -~ 
artlc\]pants: ~.nstance-ofi 'Set\] i 
\[Typical-member: 'Person\]\[ 
Thne: ~ustance-of: 'Time-interval\]\[ 
Start-time: ~J~stance-of'. 'Time-of-day\] | 
nd-time: ~nstance-0f: 'Time-of-day\] _~ 
Figure 1" Part of an object type definition. 
Discourse status 
We do not talk about all discourse objects at once. At 
any particular moment of an interaction some discourse 
objects are more salient than others because they are being 
talked about. As is well known, the way an object has been 
talked about at a certain point has consequences for how it 
can be talked about in the sequel (of. e.g. Sidner, Webber 
op. cit.). It also has consequences for how other objects 
which are related to those salient ones can be talked about. 
On the other hand there are discourse objects that have a 
particular status in virtue of being parts of the context of 
utterance. Such objects are the speaker, the addressee, the 
time of utterance and the place of utterance. A third kind 
of property that distinguishes discourse objects from one 
another concerns whether an object is part of the shared 
knowledge of the actors of the interaction or not. 
I will treat all distinctions of this kind as distinctions of 
discourse status. Objects of the first type will be referred 
to as topical and those of the second type as eentra/. There 
can be overlap between these categories, but generally they 
are different. Expressions such as my, yesterday s here pick 
out central discourse objects or objects with specific 
relations to central objects, whereas expressions such as 
his, the day be/ore, in front pick out topical objects or 
objects with specific relations to topical objects. Objects of 
the universe of discourse which are neither topical nor 
central will be referred to as knotvn. 
To keep track of changes in discourse status a 
conversational score, or score-board, is used (Lewis, 1979). 
One purpose of the score-board is to register topical and 
central discourse objects at any particular point of the 
interaction. This information must be updated for every 
new utterance. How this should be done is a difficult 
problem that I will not address here. However, in this area 
141 
we prefer simple algorithn~ to high coverage as we are not 
aiming for a complete solution to the problem of anaphoric 
reference, but for something which can be useful in 
man-machine dialogue. 
The score-board has another important duty as well, 
viz. to register expectations on user input. For 
illustrations, see below. 
Parsing and Interpretation 
The entity-oriented parsing of Hayes (1984) is proposed 
as a suitable technique for interfaces with restricted 
domains. The characteristic feature of this technique is the 
close coupling between semantic and syntactic knowledge. 
Each entity definition is coupled with a 
~SuffsceRepresentation" of that entity, i.e. information 
about how such entities are expressed in linguistic 
utterances. Thus, each object type defines its own 
sub-language as it were. This has several advantages, e.g., 
it allows for independent recognition of entities, it makes 
possible the interpretation of ill-formed input and it can 
also be supported theoretically: the language we use for 
talking about people is not the same as the language we 
use for talking about times or locations (or for performing 
various types of speech acts) and this difference is not 
merely a difference in vocabulary but also a difference in 
syntax. However, Hayes makes full use of the 
entity-language correspondences only in top-down 
recognition, i.e. in the direction from object types to 
instances. There is no attempt at expressing syntactic 
knowledge at an appropriate level of generality; every 
single entity type has its own SurfaceRepresentation so 
syntactic generalizations that hold across entities are 
neither used nor expressed. 
Tomita&Carbonell (1986), using entity-oriented parsing 
in the context of multi-lingual machine-translation for 
multiple restricted domains, propose to capture syntactic 
generalities by means of separate LFG-style grammars for 
the different languages. The grammars are kept separate 
from the entity definitions (and the dictionaries) at 
development time, but are integrated in one large grammar 
at run-time. This grammar, the rules of which are phrase 
structure rules augmented with LISP-programs for tests 
and actions, can then be parsed by a suitable algorithm for 
augmented context-free languages. 
This method presupposes that the knowledge bases that 
are integrated don't change in the course of processing. An 
NLI with dialogue capabilities must not only handle 
syntactic and semantic knowledge, however, but also 
knowledge of the universe of discourse which changes with 
every new utterance, so a different method must be used. 
Such a parser/interpreter should be able to access the 
different knowledge bases at run-time as illustrated in 
figure 2. 
PsA-ser 
Inter- 
preter 
Lexicon l' 
 o- o ogy l 
Syntax ~-~ 
Input 
Scoreboard 
Universe of 
discourse 
Object-type 
knowledge 
I~ rammatical Description 1 ontent Description J 
Figure 2: Knowledge bases for the parser. 
The output of the parser is a DOD for the input 
utterance, which contains information both about its 
syntactic structure and its content. The grammatical 
description (GD) is separated from the content description 
(CD) in accordance with the view that they result as 
evaluations of the utterance from two different, but 
complementary, perspectives. 
The content description is basically a structure of 
DODs. Thus, the same representation language can be 
used for discourse objects, object type definitions and 
content descriptions. Lexical entries as well as rules of the 
grammar are associated with descriptors which I express 
here as schemata in an LFG-style formalism. The 
construction of the content description for an input will be 
an incremental process, as far as possible based on 
unification. However, particularly in the non-syntactic part 
of the construction other, more complex operations will 
have to be used. 
The content description can best be viewed as a 
contextuaiized semantic representation. It is partially 
determined by the information supplied in the utterance, 
but is enriched in the interpretation process by the use of 
the other knowledge sources. The information in the 
constituent DODs include (i) object type and other 
properties of the corresponding discourse object; (ii) the 
discourse status of the object, and (ill) information about 
identity. 
142 
Knowledge of the universe of discourse 
Expectations - Initial hypotheses about the content 
description of an input may come from two sources. It may 
come from expectations about what is to follow or, in the 
absence of specific expectations, from the grammatical (and 
lexical) information found in the input. Utterance types are 
not identified with command types as there is nb 
one-to-one correspondence between inputs and commands 
to the background system. Instead, inputs are regarded as 
messages which are classified in terms of general 
iUocutionary categories such as assertions, questions and 
directives. However, many utterances will give whole or 
partial specifications of a command to be executed, which 
means that they are analysed as having that command as 
their topic, i.e. as (one of) the discourse object(s) that the 
interaction currently is about, possibly having some 
specific part or aspect of it as an immediate topic. 
As an example, consider the short exchange below. The 
content description of (1) is, in abbreviated form, (3). 1 
(1) U: Book a meeting with Jim Smith on Monday. 
(2) S: At what time? 
(3) 
D 
Instance-of: 'Directive 
Agent: USER 
Recipient: SYSTEM 
Action: 
Instance-of: . 'Booking 
Agent: SYSTEM 
Object: 
Flustance-of: 'Meeting 
\[Participants: ( USER, J.S ) \[I 
_ LTime: \[Week-day: Monday\]_~ _ 
As a result of this interpretation the system introduces 
two new discourse objects (apart from the utterance itself): 
(i) a booking to be executed on the background system, 
and (ii) a meeting to be booked. They are labelled, say B1 
and M1, and supplied with their descriptions. Moreover, 
both B1 and M1 are assigned topical status. The system is 
able to recognize information that it lacks for booking a 
meeting by comparing the information it has with a 
definition for a booking command. Having done this it may 
take the initiative and ask the user to supply that 
information, by outputting (2) above. In this case the next 
input from the user will be met with definite expectations, 
1 Values in capital letters are object labels obtained by special 
object modules. The other descriptors stem from the lexicon and 
the grammar (see below). 
viz. that it will be an answer relating to a topic such as 
<M1 Start-tlme>. Such expectations are registered on the 
score-board. They have effects not only on the content 
description of the next utterance, but also for the way it is 
parsed, as we may invoke an appropriate rule top-down, in 
this case a rule for the structure of a time-of-day, to see 
whether the expectations are met. 
Another case where expectations are necessary for 
solving an interpretation problem is with identifications of 
the type (4). The form of this utterance reveals it as some 
sort of assertion, but there is no way of telling from the 
words alone what the topic is. If it occurs at the beginning 
of an interaction, however, it should most likely be taken 
as information about who the user is. In this case the 
expectations don't arise from a previous utterance, but 
from general knowledge about how interactions begin. 
Knowledge about interactions is stored in the object 
type definition for interactions. This definition basically 
provides a grammar of constraints on possible interactions. 
The field in the score-board that registers expectations on 
input is maintained by a processor that has access to the 
interaction grammar. 
(4) It is Lars. 
(5) It is dry. 
Topical objects - The constituent DODs of a content 
description must include information about which discourse 
object the DOD describes. Information about identity is 
often needed for disambiguation, e.g. to make the 
appropriate reading of a polysemous word. This may 
require consulting both the score-board and object type 
definitions. Thus, to interpret (5) in a system which allows 
dry to apply to different kinds of objects, say wines and 
climate, requires that we first identify the discourse object 
accessed by the subject (via the score-board topics field) 
and then use the definition associated with its object type 
to see in what way it can be specified as dry. 
As a second example consider the case of 
PP-attachment. Wilks et al. (1985) argue (convincingly to 
my mind) that syntax generally fails to discriminate 
between alternative attachments. Instead they claim that 
correct interpretations can be made by a preferential 
approach on the basis of semantic information associated 
with the relevant verbs, nouns and prepositions. 
However, preferences based on general semantic 
evaluations are not sufficient either. Our knowledge of the 
actual discourse plays an important role. Consider (6), 
which taken in isolation is ambiguous since both meetings 
and cancellations are objects that ~happen ~ at definite 
times and therefore may be specified for time. A 
preferential approach must apply some ordering 
143 
mechanism to handle a case like this. In the strategy 
employed by Wilks et al. the first attachment tried is to 
the nearest element to the left which has a preference for 
the content of the PP. In this case it will succeed 
(assuming that meetings have a preference for temporal 
PPs). There is an interpretation of (6) which is similar to 
(71, however. This interpretation is the appropriate one if 
we consider (6) in a discourse where the question (8) ha~ 
been asked. It will also be favoured in a discourse for which 
there is a discourse object identifiable as 'the meeting' but 
no discourse object identifiable as 'the meeting on 
Monday'. This would be the case if there is only one 
topical meeting, whereas the latter expression is 
appropriate in a context where there is a set of meetings of 
the same discourse status of which only one is on Monday. 
(6) You cancelled the meeting on Monday. 
(7) You cancelled it on Monday. 
(8) When did I cancel the meeting? 
Also, the preference approach is insensitive to other 
global properties of the utterance. For instance, while it 
may be allowed to ask for information about the time of 
execution of a command, as in (81, and hence possible for 
the system to inform about it, with either of (6) or (7), it 
may be disallowed to request other executions than 
immediate ones, so that (91 and (10 / would be 
non-ambiguous as regards attachment of the final PP. 
(9) I want to cancel the meeting on Monday. 
(I0) Cancel the meeting on Monday. 
The system can handle such cases by treating either all 
directives, or some subset of directives which includes 
bookings and cancellations, as objects that obligatorily 
have their temporal information determined by the time of 
execution. Only after they have been executed should their 
execution times be available as discourse topics. 
We may also compare (10) to (11) and (12). Whereas 
(I0) is ambiguous (in isolation) (11) non-ambiguously 
means that the meeting is on Monday, whereas (12) 
non-ambiguously means that the cancellation should be 2 
performed on Monday. 
(11 ! Cancel the one on Monday. 
(12) Cancel it on Monday. 
The pronouns must also be contextually appropriate, of 
course. The difference between them coincides well with 
the difference between the two possible interpretations of 
(10); (12) can be used if there is only one topical meeting 
2 Interestingly, Swedish is different on this point. Avboka det p~ 
mlmdag could mean either "Cancel it on Monday" or "Cancel 
that (= the one) on Monday'. 
and (I1) can be used if there is a set of topical meetings 
(cf. Webber (1984)). However, the differences in 
PP-attachment between (11) and (12) can be stated 
already in the syntax as one is categorized as an N that 
allows for PP-complements, whereas /t is categorized as an 
N (or NP) that does not permit PP-complements. 
Syntax and the Lexicon 
It may be suggested that for an NLI the grammatical 
structure of an utterance has no intrinsic interest. 
However, most linguistic interactions involving humans 
seem to develop formal constraints over and above those 
needed to differentiate between message types and there is 
no reason why this should not hold for NLIs as well. 
Although (13) is interpretable it is not formed according to 
standard norms for English and it might not disturb users 
if it is disallowed. 
(13) On Monday a meeting with Jim Smith book. 
The primary motivation for constructing the GD, 
however, is the close correspondence between grammatical 
constituents and elements of the CD. The GD thus serves 
as an aid to interpretation. Moreover, we need a syntactic 
level of representation to take care of strictly syntactic 
restrictions on phenomena such as reflexivization and 
long-distance dependencies. 
It must be noted though that the interest in 
grammatical descriptions is not an interest in the 
structural potential of constructions, but with the structure 
appropriate for the corresponding content description on a 
particular occasion of use. While the grammar taken in 
isolation may allow several different GDs of a given input, 
the GD for a particular utterance is constructed in parallel 
with the CD using the other knowledge bases as well. 
As said above an LFG-style formalism for the linguistic 
part of the description can be used, where the constraints 
on DODs that words and constructions are associated with 
can be formulated in the same way as functional 
constraints in LFG. 3 The GD and the CD are constructed 
incrementally and in tandem using a chart-parser for 
recognition of syntactic constituents. 
To find the contextually appropriate interpretations and 
reduce the combinatorial explosion of alternative parses the 
parser is interacting with other processors that I call object 
3 Cf. the use of situational schemata in Fenstad et al. (1986) In 
the illustrations below I use no f-structure level at all. 
Functional information is instead incorporated at the c-structure 
level. I do this here for the sake of brevity only and no 
theoretical claims are being made. 
144 
modules. Their purpose is to link DODs with discourse 
objects and evaluate the information in DODs against 
existing expectations. When a constituent is syntactically 
complete (or potentially complete) control is given to an 
object module which seeks to establish an object that is 
described by the DOD derived by the syntactic parser. 
Such a scheme should be based on a theory about thi~ 
correspondence between syntactic structure and discours~ 
object relations. The closer the correspondence the better it 
would be, but we definitely do not have an isomorphic 
correspondence. It seems, however, that the 
correspondences obey locality conditions of the kind that 
can be specified in the basic schemata of the 
LFG-formalism, the following being the most common 
ones: 
Embedding: T = 
Isomorphy: (T Attr) = 
Discrimination: (T Attr) = 'Value 
Percolation: (T Attr) = (J. Attr) 
(T Attr2) = (T Attrl Attr2) 
Similarly, we need a theory for the possible relations 
between lexical categories and constituent structure on the 
one hand, and for the relation between lexical items and 
DODs on the other. The relation between lexical heads and 
major syntactic constituents is in LFG spelled out as a 
condition that any f-structure must contain a semantic 
form as the value of the attribute PRED in order to be 
coherent and complete (Kaplan&Bresnan, 1982: 211f), 
where PRED-attributes primarily go with nouns, verbs and 
adjectives. In the present framework a similar 
correspondence can be stated in terms of DODs and the 
attribute Instance-of. However, we should allow 
Instance-of-deecriptors to be associated with more than 
one word of a constituent as long as they have compatible 
values. This should be the case for expressions such as Mr. 
Jim Smith, where all words specify different attributes of a 
person, and for an adjective such as dry in (5) when it 
applies to wines. 
I regard grammar rules as defining the internal 
composition of significant syntactic objects. By 'significant' 
is then meant significant for determining object 
descriptors. This means that I favour isomorphy .and 
embedding as the local structural correspondences between 
GDs and CDs. The internal composition usually specifies 
one or more positions for lexicM heads and other 
distinguished markers for that type of constituent. Rules 
for declarative sentences and NPs (which hold good for 
both Swedish and English) are shown below. VCOMP and 
NCOMP are variables over regular expressions of 
complements that are assigned variables from the lexical 
head. 
RI: U -> {S\[Decl\]/S{Imp\]/...) 
R2: S\[Decl\] -> NP\[Subj\] V\[Fin\] VCOMP SAD J* 
R3: NP -> (DET/NP\[POSs\]) AP* N NCOMP REL* 
As soon as a lexical head (or other marker) for a 
syntactic constituent has been recognized, such a 
constituent as well as a corresponding DOD can be 
, postulated, the latter taking descriptors from both lexical 
head and structure. Associated with the rule that 
introduces declarative clauses we would have schemata 
such as: 
DSI: (T Instance-of) = 'Assertion 
(T Agent) = <Score-board Speaker> 
(T Recipient) = <Score-board Addressee> 
(T Event) = 
A lexical entry for a word gives for each one of its 
different uses a syntactic category, a morphological 
sub-category (omitted here), a set of descriptive schemata 
and a structure of possible complements with associated 
descriptive schemata. The verb cancel has as one of its 
entries: 
cance~ V; (T Instance-of) = 'Cancel 
NP\[Subjl; (T Agent) = 
VCOMP: NP; (T Object) = 
PP; (T Time) = 
From DODs to Discourse Objects 
The linguistic information can not give us a discourse 
object. Instead we need special modules that attempt to 
link DODs to discourse objects. There are different types of 
relations between DODs and discourse objects, however. 
Certain DODs should be linked to existing discourse 
objects (anaphoric pronouns, Proper Nouns), others should 
be used to constitute a discourse object (main declarative 
clauses, indefinite NPs in certain positions) and still others 
should be linked to a discourse object only indirectly (NPs 
and APs in predicative positions). Such information is also 
associated with words and constructions and we may 
encode it by special-purpose descriptors. 
Suppose information concerning discourse status is 
encoded by means of an attribute Status with values such 
as Topical, Speaker, Addressee. An NP containing a 
definite article or the pronoun it is assigned such a 
descriptor from lexical entries of the following sort: 
145 
the; DET; { (T Status)=Topical 
/ (T Status)='Known } 
/t; { NP; (T Status)=Topical 
(T Sex)='Neutral / NP\[it\]; --} 
If a DOD has the descriptor \[Status: Topical\] a 
module is activated which attempts to unify the given 
DOD (minus the Status-descriptor) with the DODs of the 
objects in the seore-board field for topical objects. If this 
succeeds for exactly one of the topical objects, that object 
is chosen as the object picked out by the given DOD. We 
mark this on the DOD by assigning that object (i.e. its 
label) as value of a special attribute, say Picks. When the 
DOD is thus completed control is given back to the 
syntactic parser. 
In the case of (4) such a matching would fail. Parsing 
can still continue with an alternative analysis of it as, say a 
purely formal element without links to a discourse object. 
An object module may also be called to resolve 
structural ambiguities. In a parsing of (6) the syntactic 
processing would reach a state in which an ambiguity 
cannot be resolved on syntactic grounds. Let us assume the 
following rules and lexical entries in addition to those 
already stated. 
R4: PP\[p\]-> P\[p\] NP 
R5: SADJ = {PP\[on\]/...} 
meeting; N; (T Instmace-of) = 'Meeting 
NCOMP: PP\[with\]; 
E (7 Participants) 
PP; (T Time) = 
Thus, the DOD associated with the PP on Monday can 
be consumed either by the DOD describing a topical 
meeting or the DOD describing the cancellation. If we 
match grammatically obtained DODs at every possible 
point of completion we would give control to the 
ecore-board processor as soon as we have found the phrase 
the meeting ignoring potential complements. The DOD 
would then be: 
astance-of: 'Meeting 1 
tatus: Topical 
If there is only one topical meeting, this match would 
succeed and we could then complete the constituent and 
attach it under the declarative S. This would also mean 
that NCOMP is set to NIL and that the PP will be 
consumed by the verb. If there is no unique match in the 
score-board at this point, control is again given to the 
parser which looks for a PP-complement to the noun. It 
will fred one, include its DOD in the meeting-DOD and 
again give control to the score-board processor. If there is 
now a unique match, parsing and interpretation will be 
completed succesfully; otherwise it will fail. 
CONCLUSIONS 
If we believe that users of NLIs think in terms of "doing 
things to things" and want to talk about those things in 
the same way as in ordinary language, e.g., by using 
pronouns and ellipsis, the NLI itself should be able to 
"think" in terms of things and understand when they are 
being talked about and how their saliency influence 
interpretation. Thus, an internal object-oriented 
representation language is suitable and a parser/interpreter 
that can make use of some knowledge about current 
discourse objects a necessity. As for the methods sketched 
briefly in this paper further work will be needed to 
determine whether they are adequate for their task. 
ACKNOWLED GE1VIENTS 
I want to thank one of my reviewers for valuable 
comments on the draft version. As I am not sure that he 
wishes to be associated with the contents of this paper I 
shall let him remain anonymous. 
REFERENCES 
Brady, Michael and Berwick, Robert C. (1984): 
Computational Models of Discourse. Second printing. The 
MIT Press. 
Dahlbgck, Nils and J6nsson, Arne (1986): A System for 
Studying Human.Computer Dialogues in Natural Language. 
Research report LITH-IDA-R-86-42, Link6ping University, 
Department of Computer and Information Science. 
Fenstad, Jens Erik, Halvorsen, Per-Kristian, Langholm, 
Tore and van Benthem, Johan (1986): Equations, 
Schemata and Situations: A framework for linguistic 
semantics. CSLI and Xerox Palo Alto Research Center. 
Hayes, Philip J. (1984): Bntity-Oriented Parsing. 
Department of Computer Science, Carnegie-Mellon 
University. Also in IOtA International Conference on 
Computational Linguistics, Stanford, 1984, pp. 212-217. 
146 
Kapla, n, R. & Bresnam, J. (1982): Lezical-Functional 
Grammar: A Formal System for Grammatical 
Representation. In Bresnan (ed.) (1982) The Mental 
Representation of Grammatical Relations. The MIT Press, 
Cambridge, Ma~. pp. 173-281. 
Lewis, David (1979): Scorekeeping in a Language Game. In 
R. B~uerle, U. Egli and A. yon Stechow (eda.): Semantics 
from Different Pointe of View. Springer-Verlag, 1979: 
172-187. 
Sidner, Candace L. (1984): Focusing in the comprehension 
of definite anaphora. In Brady&Berwick pp. 267-330. 
Tomita, Ma~aru, and Csrbonell, Ja~me G, (1986): Another 
Stride Towards Knowledge-Based Machine Translation. 
Proceedings of COLING '80, University of Bonn, pp. 
633-38. 
Webber, Bonnie L. (1984): 5o what can we talk about nowf 
In Brady&Berwick pp. 331-371. 
Wilks, Yorick, Huang, Xiuming & Fass, Dan (1985): 
Sgntaz , Preference and Right Attachment. In Proceedings 
of the Ninth International Joint Conference of Artificial 
Intelligence, Los Angeles, 1985, pp. 779-784. 
147 
