ALGORITHM FOR AUTOMATIC INTERPRETATION OF NOUN SEQUENCES 
Lucy Vanderwende 
Microsoft Research 
lucyv@ microsoft.corn 
ABSTRACT 
This paper describes an algorithm for 
automatically interpreting noun sequences in 
unrestricted text. This system uses broad- 
coverage semantic information which has been 
acquired automatically by analyzing the 
definitions ira an on-line dictionary. Previously, 
computational studies of noun sequences made 
use of hand-coded semantic information, and they 
applied the analysis rules sequentially. In 
contrast, the task of analyzing noun sequences in 
unrestricted text strongly favors an algorithm 
according to which the rules are applied in 
parallel and the best interpretation is determined 
by weights associated with rule applications. 
1. INTRODUCTION 
The inte~opretation of noun sequences 
(henceforth NSs, and also known as noun 
compounds or complex nominals) has long been a 
topic of research in natural language processing 
(NLP) (Finin, 1980; Sparck Jones, 1983; 
Leonard, 1984; Isabelle, 1984; Lehnert, 1988; and 
Riloff, 1989). The challenge in analyzing NSs 
derives from the semantic nature of the problem: 
their interpretation is, at best, only partially 
recoverable from a syntactic or a morphological 
analysis of NSs. To arrive at an interpretation of 
plum sauce which specifies that plum is the 
Ingredient of sauce, or of knowledge 
representation, specifying that knowledge is the 
Object of representation, requires semantic 
information for both the first noun (the modifier) 
and the second noun (the head). 
In this paper, we are concerned with 
interpreting NSs which are composed of two 
nouns, ira absence of the context in which the NS 
appears; this scope is similar to most of the 
studies mentioned above. The algorithm for 
interpreting a sequence of two nouns is intended 
to be basic to the algorithm for interpreting 
sequences of more than two nouns: each pair of 
NSs will be interpreted in turn, and the best 
interpretation forms a constituent which can 
modify, or be modified by, another noun or NS 
(see also Finin, 1980). There is no doubt that 
context, both intra- and inter-sentential, plays a 
role in determining the correct interpretation of a 
NS, since the most plausible interpretation in 
isolation might not be the most plausible in 
context. It is, however, a premise of the present 
system that, whatever the context is, the 
interpretation of a NS is always available in the 
list of possible interpretations. A NS that is 
ah'eady listed in an on-line dictionary needs no 
interpretation because the meaning can be derived 
from its definition. 
In the studies of NSs mentioned above, the 
systems tbr interpreting NSs have relied on hand- 
coded semantic information, which is limited to a 
specific domain by the sheer effort involved in 
creating such a semantic knowledge base. The 
level of detail made possible by hand-coding has 
led to the development of two main algorithms 
for the automatic interpretation of NSs: concept 
dependent and sequential rule application. 
The concept dependent algorithm (Finin, 
1980) requires each lexical item to contain an 
index to the rule(s) which should be applied when 
that item is part of a NS; it has the advantage that 
only those rules are applied for which the 
conditions are met and each noun potentially 
suggests a unique interpretation. Whenever the 
result of the analysis is a set of possible 
interpretations, the most plausible one is 
determined on the basis of the weight which is 
associated with a role fitting procedure. The 
disadvantage of this approach is that this level of 
lexical information cannot be acquired 
automatically, and so this approach cannot be 
used to process unrestricted text. 
The algorithm for sequential rule application 
(Leonard, 1984) focuses on the process of 
determining which interpretation is the most 
plausible; the fixed set of rules are applied in a 
fixed order and the first rule for which the 
conditions are met results in the most plausible 
interpretation. This algorithm has the advantage 
that no weights are associated with the rules. The 
disadvantage of this approach is that the degree to 
which the rules are satisfied cannot be expressed, 
and so, in some cases, the most plausible 
782 
interpretation of an NS will not be produced. 
Also, section 4 will show that this algorithm is 
suitable only when the sense of each noun is a 
given, a situation which is not true for processing 
unrestricted text. 
This paper introdt.ces an algorithm which is 
specifically designed for analyzing NSs in 
unrestricted text. The task of processing 
unrestricted text has two consequences: firstly, 
hand-coded semantic information, and therefore a 
concept dependent algorithm, is no longer 
feasible; and secondly, the intended sense of each 
noun cau no longer be taken as a given. \]'he 
algorithm described here, therefore, relies on 
semantic information which has been extracted 
automatically fi'om an on-line dictionary (see 
Montemagni and Vanderwende, 1992; l)ohm et 
al., 1993). This algorithm manipulates a set of 
general rules, each of which has an associated 
weight, and a general procedure for matching 
words. The result of this algorithm is an ordered 
set of interpretations and partial scnse- 
disambiguation of the nouns by taking note of 
which noun senses were most relevant in each of 
the possible interpretations. 
We will begin by reviewing the 
chtssification schema for NSs described in 
Vanderwende (1993) and the type of general rules 
which this algorithm is designed to handle. The 
matching procedure will be described; by 
introducing a separate matching procedure, the 
rules in Vanderwende (1993) can be organized in 
such it way as to make the algorithm more 
efficient. We will then show the algorithm t'or 
rule application in delail. This algorithm differs 
fiom I,conard (1984) by applying all of the rules 
before determining which interpretation is the 
most plausible (effectively, a parallel rule 
application), rather than determining the best 
interpretations by the order in which the rules ate 
applied (a serial rule application). In section 4, we 
will provide examples which illustrate that a 
parallel algorithm is required when processing 
unrestricted, uudisambiguated text. Finally, lhe 
results of applying this algorithm to a training 
and a test corpus of NSs will be presented, along 
with a discussion of these results and fnrther 
directions fox" research in NS analysis. 
1.1 NS interpretations 
'Fable 1 shows a classil'ication schema for 
NSs (Vande,wende, 1993) which accounts for 
most of the NS classes studied previously in 
theoretical linguistics (Downing, 1977; Jespersen, 
1954; Lees, 1960; and Levi, 1978). The relation 
which holds between the nouns in a NS has 
conventionally been given names such as Purpose 
or Location. The classification schema that is 
used in this system has been formulated as wh- 
questions. A NS 'can be classified according to 
which wh-question the modifier (filwt noun) best 
answers' (Vanderwende, 1993). Deciding how a 
NS should be classified is not at all clear and we 
need criteria for judging whether a NS has been 
classified appropriately. The formulation of NS 
classes its wh-questions is intended to provide at 
least one criterion for judging NS classification; 
other criteria are provided in Vanderwende 
(1993). 
Table I. Classification schema for NSs 
Relation Conventional Name Example 
Who/what? 
Whom/what? 
Where? 
When? 
Whose? 
__. What is it part of? 
What are its .parts? 
What kind of? 
How? 
What for? 
Made of what? 
What does it cause'? 
What causes it? 
Subject 
Object 
Locative 
Time 
Possessive 
Whole-Part 
Part-Whole 
Equativc 
Instrtnnent 
Purpose 
Material 
Causes 
Caused-by 
press report 
accident report 
field mouse 
night attack 
family estate 
duck loot 
daisy chain 
flounder fish 
paraffin cooker 
bird sanctuarL_ 
alligator shoe 
disease germ ___ 
drug death 
783 
1.2 General rules for NS analysis 
Each general rule call be considered to be a 
description of the configuration of semantic and 
syntactic attributes which provide evidence for a 
particular NS interpretation, i.e., a NS 
classification. Exactly how these rules are applied 
is the topic of this paper. Typically, the general 
rules correspond in a many-to-one relation to the 
number of classes in the classification schema 
because more than one combination of semantic 
attributes can identify the NS as a member of a 
particular class. This is illustrated in Table 2, 
which presents two of the rules tbr establishing a 
'What for?' interpretation. 
The first rule (H1) tests whether the 
definition of the head contains a PURPOSE or 
INSTRUMENT-FOR attribute which matches 
(i.a., has the same lemlna as) the modifier. When 
this rule is applied to the NS bird sanctuary, the 
rule finds that a PURPOSE attribute has been 
identified automatically in the definition of the 
head: sanctuary (L n,3) 'an area for birds or other 
kinds of animals where they may not be hunted 
and their animal enemies are controlled'. (All 
examples are from the Longman Dictionary of 
Contemporary English, Longman Group, 1978.) 
The values of this PURPOSE attribute are bird 
and animal. The rule HI verifies that the 
definition of sanctuary contains a PURPOSE 
attribute, and that one of its values, namely bird, 
has the same lemma as the modifier, the first 
noun. 
The second rule (H2) tests a different 
configuration, namely, whether the definition of 
the head contains a LOCATION-OF attribute 
which matches the modifier; the example bird 
cage will be presented in section 2. 
These rules are in a notation modified from 
Vanderwende (1993, pp. 166-7). Firstly, the rules 
have been divided into those that test attributes on 
the head, as rules HI and H2 do, and those that 
test attributes on the modifier. Secondly, 
associated with each rule is a weight. Unlike in 
Vanderwende (1993), this rule weight is only part 
of the final score of a rule application; the final 
score of a rule application is composed of both 
the rule weight and the weight returned from the 
matching procedure, which will be described in 
the next section. 
2. THE MATCHING PROCEDURE 
Matching is a general procedure which 
returns a weight to reflect how closely related two 
words are, in this case how related the value of an 
attribute is to a given lemma. The weight returned 
by the matching procedure is added to the weight 
of the rule to arrive at the score of the rule as a 
whole. Ill the best case, the matching procedure 
finds that the lemma is the same as the value of 
tile attribute being tested. We saw above that ill 
the NS bird sanctuary, the lnodifier bird has the 
same lemma as the value of a PURPOSE attribute 
which can be identified in the definition of the 
head, sanctualy. The weight associated with snch 
an exact match is 0.5. Applying rule H1 ill Table 
2 to the NS bird sanctuary has an overall score of 
1; the match weight 0.5 added to the rule weight 
0.5. 
When an exact match cannot be found 
between the lemma and the attribute value, the 
matching procedure can investigate a match given 
semantic information for each of the senses of the 
lemma. (Only in the worst case would this be 
equivalent to applying each rule to each 
combination of modifier and head senses.) Of 
course the HYPERNYM attribute will be useful 
to find a match. Applying rule HI to the NS owl 
sanctuary, a match is found between the 
PURPOSE attribute in the definition of sanctuary 
and the modifier owl, because the definition of 
owl (L n,l): 'any of several types of night bird 
with large eyes, supposed to be very wise', 
identifies bird (one of the values of sanctual:v's 
PURPOSE attribute) as the HYPERNYM of owl. 
Whenever the HYPERNYM attribute is used, the 
weight returned by the matching procedure is 
only 0.4. 
Table 2. Rules for a 'What for?' interpretation 
SENS class 
What for? 
Rule 
name. 
HI 
Modifier 
.attributes 
Head attributes 
match 
I 
Example 
match PURP()SE, water heater, 
INSTRUMENT- bird sanctuary 
FOR 
LOCATION-OF bird cage, 
~e cam_p 
Weight 
'0.5 
784 
Other semantic attributes are also relewmt 
for l'inding a match. Fig. I shows graphically how 
the attribute HAS-PART can be used to establish 
a match. One of the 'Who~What?" rules tests 
whether any of the verbal senses of the head has a 
BY-MEANS-OF attribute which lnatches the 
modifier. In the verb definition o1' scratch (I, v, I): 
'to rub and tear or mark (a surface) with 
something pointed or rough, as with claws or 
fingernails', a P,Y-MI';ANS-OF attribute can be 
idenlified with claw and.fingernail as its values, 
neither of which match the modifier norm cat. 
Now the ma|ching procedure investigates the 
senses of cat attempting to find a match. The 
definition of eat (L n, 1): 'a small animal with soft 
fur and sharp teeth and claws (nails) .... ' klentil'ies 
claw (one o1' scratch's 13 Y-MF, ANS-OF 
altributes) as one of the wflues of ItAS-PART, 
thus establishing the match shown in Fig. I. The 
weight associated with a match using \[tAS- 
PART, PART-()F, ltAS-MATERIAL, or 
MATERIAL-OF is 0.3. 
HAS-PAre >>K ? /~/ BY-MEANS-OF 
Q cat -) (-scratch 
Fig. 1. 'Who/what?' interpretation for cat scratch 
with cat (/, n, I) and scratch (l, v, 1) 
lqg. 2 shows how also the attrilmtes IIAS- 
OBJECT and HAS--SUfLIECT can be used; this 
type of match is required when a rule calls for a 
match between a lemma (which is a noun) and an 
attribute which typically has a verb as its value, 
since we can expect no link between a noun and a 
verb according to hypernymy or any part relation. 
In the definition of cage (l, n, I): 'a framework of 
wires or bars m which animals or birds may hc 
kept or carried', a IX)CATION-.OF attribute can 
be identified, with as its value the veflm keep and 
carry and a nested HAS-()BJI~;CI" attribute, with 
animal and bird as its wflue; it is the HAS- 
()BJECT attribute which can match the modifier 
noun bird. A match using the HAS-OBJECT or 
IlAS-SUBJI';CT attribute carries a weight of 0.2. 
f. ~oo.,, ) 
~/~ • I OCATION-OF 
Fig. 2. 'What for?' interpretation for bird cage 
with cage (L n, l) 
Even when alternate matches are being 
investigated, such as a match using \[I\[AS- 
OBJECT, the senses of the lemma can still be 
examined. In this way, a 'What for?' 
interpretation can also be determined for the NS 
canat:v cage, shown in Fig. 3; the weight for this 
type of link is O. 1. 
IIAS-OBJECT 
C '~'~;) < <_ ,oo,, > 
/\[~YPE,~NYM "~ LOCATION-OF 
Fig. 2. 'What for?' interpretation for canary cage 
with canary (L n, 1 ) and cage (L n, 1) 
In Vanderwende (1993), the rules themselves 
specified how to find the indirect matches 
described above. By separating the matching 
information from the information relevant to each 
role, the matching can be applied more 
consistently; but equally important, the roles 
specify only those semantic attributes that 
indicate a specific interprelation. 
3. ALGORITHM FOR APPLYING RULI,;S 
The algoritlm\] controls how the set of 
general rules will be applied in order to interpret 
NSs in unrestricted text. Given that a separate 
procedure for matching exists, the rules are 
naturally formulated as conditions, in the form of 
a semantic attribute(s) to be satisfied, on either 
the modifier or head, but not necessarily on both 
at the same time. This allows the rules lo be 
divided into groups: modifier-based, head-based, 
and deverbal-head based. NSs with a deverbal 
head require additional conditions in the rules; if 
deverbal-head based rules were applied on par 
with the headqmsed rules, the deverbal-head rules 
wouM apply far too often, leading to spurious 
interpretations, because in English nouns and 
verbs are often homographs. 
785 
The algorithm for interpreting NSs has four 
steps: 
1. apply the head-based rules to each of 
the noun senses of the head and the lemma of 
the modifier 
2. apply the modifier-based rules to each 
of the noun senses of the modifier and the 
lemma of the head 
3. if no interpretation has received a 
weight above a certain threshold, then apply 
the deverbal-head rules to each of the verb 
senses of the head and the lemma of the 
modifier 
4. order the possible interpretations by 
comparing the weights assigned by the rule 
applications and return the list in order of 
likelihood 
The semantic attributes which are found in 
the head-based conditions are: LOCATED-AT, 
PART-OF, HAS-PART, HYPERNYM, BY- 
MEANS-OF, PURPOSE, INSTRUMENT-FOR, 
LOCATION-OF, TIME, MADE-OF, ROLE, 
CAUSES and CAUSED-BY. The semantic 
attributes which are found in the modifier-based 
conditions are: SUBJECT-OF, OBJECT-OF, 
LOCATION-OF, TIME-OF, HAS-PART, PART- 
OF, HYPERNYM, MATERIAL-OF, CAUSES 
and CAUSED-BY. The semantic attributes in the 
deverbal-head based conditions are: HAS- 
SUBJECT, BY-MEANS-OF, and HAS-OBJECT. 
In Vanderwende (1993), it was suggested 
that each rule is applied to each combination of 
head sense and modifier sense. If the modifier has 
three noun senses and the head has four noun 
senses, then each of the 34 general rules would 
apply to each of the (3x4) possible combinations, 
for a total of 408 rules applications. With the 
current algorithm, if the modifier has three noun 
senses and the head has four noun senses, then 
first the eleven modifier roles apply (3xl 1), then 
the sixteen head rules apply (4xl6), and if the 
head can be analyzed as a deverbal noun, then 
also the seven deverbal-head rules apply (4x7), 
for a total of 125 rule applications. Only after all 
of the rules have applied are the possible 
interpretations ordered according to their scores. 
It may seem that we have made the task of 
interpreting NSs artificially difficult by taking 
into consideration each noun sense in the 
modifier and head; one might argue that it is 
reasonable to assume that these nouns could be 
sense-disambiguated before NS analysis. We are 
not aware of any study which describes sense- 
disambiguation of the nouns in a NS. On the 
contrary, Braden-Harder (1992) suggests that the 
results of disambiguation can be improved when 
relations such as verb-object, purpose, and 
location, are available; these relations are the 
result of our NS analysis, not the input. 
4. PARALLEL VERSUS SERIAL RULE 
APPLICATION 
As we have seen above, the overall score for 
each possible interpretation is a combination of 
the weight of a rule and the weight returned by 
the matching procedure. A rule with a relatively 
high weight may have a low score overall if the 
match weight is very low, and a role with a 
relatively low weight could have a high overall 
score if the match weight is particularly high. It is 
therefore impossible to order the rules a priori 
according to their weight. 
In Leonard (1984), the most plausible 
interpretation is determined by the order in which 
the rules are applied. By ordering the 'search for a 
material modifier' ahead of the 'search for a 
related verb', the interpretations of both silver pen 
and ink pen will be the same, given that both 
silver and ink are materials. In fact, only silver 
pen is correctly analyzed by the 'search for a 
material modifier' rule, while the correct 
interpretation of ink pen would have used the 
'search for a related verb'. 
The problem with rule ordering is 
compounded when more than one sense of each 
noun is considered. In Leonard's lexicon, pen\[l\] 
is the writing implement and pen\[2\] is the 
enclosure for keeping animals in. By ordering a 
'search for a related verb' ahead of a 'search for a 
locative', the interpretation of the NS bull pen is 
incorrect: 'a pen\[1\] that a bull or bulls writes 
something with'. Less likely is the correct 
locative interpretation 'a pen\[2 \] Jbr or containing 
a bull or bulls'. 
In our system, the most likely interpretations 
of bull pen are ordered correctly because, for the 
locative interpretation, we find meaningful 
matches in the definitions of bull and pen: the 
definition of pen (L n,l): 'a small piece of land 
enclosed by a fence, used esp. for keeping 
animals in', identifies a PURPOSE attribute, with 
the verb keep and a nested HAS-OBJECT animal 
as its values. The HAS-OBJECT animal can be 
matched with the modifier lemma bull, because 
one of the HYPERNYMs of bull (L n,2) is 
animal. For the related verb interpretation, 
786 
however, we find no match between the typical 
subjects the verb related to pen, namely write, 
and the modifier bull; a 'Who/What?' 
interpretation is only possible because bull is an 
animate, and, by default, animates can be the 
subject of a verb. 
We must conclude that what is important is 
the degree to which there is a match between the 
values of these attribules and the lemma, and not 
merely the presence or absence of semantic 
attributes. Only after all of the rttles have been 
applied can the most plausible interprelation be 
determined. 
5. TEST, RESULTS AND I)ISCUSSION 
The results that arc under discussion were 
obtained on the basis of semantic information 
which was automatically extracted from Longman 
I)ictionary of Contemporary English (I~I)OCE) as 
described in Montemagni and Vanderwende 
(1992) 1 . The semantic information has not been 
altered in any way fl'onl its automatically derived 
form, and so there are still errors: for the 94,()00 
attribute clusters extracted fl'om nearly 75,000 
single noun and verb definitions in L1)()CE, we 
estimate the accuracy to be 78%, with a margin of 
error of +/- 5% (see Richardson et al., 1993). 
A training corpus of 100 NSs was collected 
\[rein lhc examples of NSs in lhe 1)revious 
literature, to ensure that all known classes of NSs 
are handled in this system. These results were 
expected to be good because these NSs were used 
to develop the rules and their weights. The 
system successfully identified the most likely 
interpretation for 79 of the 100 NSs (79%). Of the 
remaining 21 NSs, tile most plausible 
interpretation was alnong the possible 
interpretations 8 times, (8 %), and no 
interpretation at all was given for 4 NSs (4 %). 
The test corpus consisted of 97 NSs from the 
tagged version of the Brown corpus (I;rancis and 
Kucera, 1989), to ensure the adequacy of 
applying this approach to unrestricted test; the 
results for an expanded test corpus will be 
reported in Vanderwende (in preparation). Tbe 
system currently identified successfully II1e most 
likely inlerpretation lor 51 of the 97 NSs (52%). 
()1' the remaining 46 NSs, the most likely 
interpretation was presented second for 21 NSs 
IAlthough 1,1)OCE includes somc semantic 
information in the form of box codes and subject 
codes, these were not used in this system. This 
approach is designed t() work with semantic 
information from any dictionary. 
(22 %); when first and second interpretations 
were considered, the system was successful 
approximately 74% of the time. A wrong or no 
interpretation was given for 25 NSs (26 %1). Upon 
examination of these results, several areas for 
improvement arc suggested. First is to improve 
lhe semantic information: Dolan et al. (1993) 
describes a network of semantic information, 
given not only the definition of the Icxical entry 
but also all of the other definitions which have a 
labeled relation to that entry. 
Secondly, while the NS classification 
proposed in Vanderwende (1993) proves to be 
adequate for analyzing NSs in tmrestrictcd text, 
an additional 'What about?' class, suggested in 
Levi (1978), may be justified. In the current 
classification schema, NSs such as cigare.tte war 
and history confi, rence have been considered 
'Whom/what?' NSs given tim verbs that are 
associated with the head, fight and eonJer/talk 
about respectively. In unrestricted text, similar 
NSs are quite fi'equent, for example university 
policy, prevention program, care plan, but the 
definitions of the heads do not always specify a 
related verb. The bead definitions for policy, 
program and plan, however, do allow a IIAS- 
TOPIC semantic feature to be identified, and this 
IIAS-TOPIC can be used to establish a 'What 
about?' interpretation. 
Applying this algorithm to previously 
tmseen text also produced a very promising 
result: the verbs that are associated with nouns in 
their definitions (i.e., role nominals in lqnin, 
1980) are being used often and correctly to 
produce NS interpretations. While some rules had 
been developed to handle obvious cases in the 
training corpus, how often the conditions on these 
rules would be met could not be predicted. In 
fact, such NS interpretations are frequent. For 
example, the NS wine cellar is analyzed as a 
'What for?' relation with a high score, and the 
system provides as a paraphrase: cellar w,~ich is 
for storing wine, given the definition of cellar (1, 
n, 1): 'an undergrotmd room, ttsu. used for storing 
goods; basement'. This result is promising for 
two reasons: first, by analyzing the definitions 
(and later also the example sentences) in an on-- 
line dictionary, we now have access to a non- 
handcoded source of semantic information which 
includes the verbs and their lelation to the notms, 
essential for determining role nominals. Second, 
the related verbs are used to construct the 
paraphrases of a NS, and doing so makes a 
general interpretation such as 'What lot?' more 
787 
specific, e.g., a service office is not an office for 
service, but an office for ~ service, and a 
vegetable market is not a market for vegetables, 
but a market for buying and selling vegetables. 
Enhancing the general interpretations with the 
related verb(s) approximates at least in part 
Downing's observation that the types of relations 
that can hold between the nouns in a NS are 
possibly infinite (Downing, 1977). 
6. CONCLUSIONS 
Our goal is to create a system for analyzing 
NSs automatically on the basis of semantic 
information extracted automatically from on-line 
resources; this is a strong requirement for NLP as 
it moves its focus away from technical 
sublanguages towards the processing of 
unrestricted text. We have shown that processing 
NSs in unrestricted text strongly favors an 
algorithm which is comprised of a set of general 
rules and a general procedure for matching two 
words, each of which have associated weights. 
This algorithm must apply all of the rules before 
the most plausible NS interpretation can be 
determined. 
Several directions for further research can be 
pursued within this approach: a methodology for 
automatically assigning the weights associated 
with the rules and the matching procedure, 
following Richardson (in preparation), and a 
methodology for incorporating the context of the 
NS into the analysis of NS interpretations. We are 
also pursuing the acquisition of semantic 
reformation which is not already available, along 
the same lines as extracting information 
automatically from on-line dictionaries. 
Acknowledgements: I would like to extend my 
thanks to the members of the Microsoft NLP 
group: George Heidorn, Karen Jensen, Bill 
Dolan, Joseph Pentheroudakis, Diana Peterson, 
and Stephen Richardson. 
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