Encoding information on adjectives in a lexical-semantic net 
for computational applications 
Antonietta ALONGE 
Istituto di Linguistica 
Computazionale, CNR 
Area della Ricerca di Pisa 
Via Alfieri 1, Loc. S. Cataldo 
Ghezzano 56010 (PI) - ITALY 
antoalonge@libero.it 
Francesca BERTAGNA 
Consorzio Pisa Ricerche 
Via S. Maria 40 
Pisa 56100 - ITALY 
F.Bertagna@ilc.pi.cnr.it 
Nicoletta CALZOLARI 
Istituto di Linguistica 
Computazionale, CNR 
Area della Ricerca di Pisa 
Via Alfieri 1, Loc. S. Cataldo 
Ghezzano 56010 (PI) - ITALY 
glottolo@ilc.pi.cnr.it 
Adriana ROVENTINI 
Istituto di Linguistica 
Computazionale, CNR 
Area della Ricerca di Pisa 
Via Alfieri 1, Loc. S. Cataldo 
Ghezzano 56010 (PI) - ITALY 
adriana@ilc.pi.cnr.it 
Antonio ZAMPOLLI 
Istituto di Linguistica 
Computazionale, CNR 
Area della Ricerca di Pisa 
Via Alfieri 1, Loc. S. Cataldo 
Ghezzano 56010 (PI) - ITALY 
pisa@ilc.pi.cnr.it 
Abstract 
The goal of this paper is to describe how the 
EuroWordNet framework for representing 
lexical meaning is being modified within an 
Italian National Project in order to include 
information on adjectives. The focus is on 
the 'new' semantic relations being encoded 
and on the revisions we have made to the 
EuroWordNet Top Ontology structure. We 
also briefly discuss the utility of the 
information which is being encoded for 
computational applications. 
Introduction 
The Princeton WordNet (henceforth WN) is a 
lexical semantic network in which the meanings 
of words are represented in terms of their 
conceptual and lexical relations to other words. 
The basic notion around which it is developed is 
that of a synset (synonyms set), i.e. a set of 
words with the same Part-of-Speech (PoS) that 
can be interchanged in a certain context. Various 
conceptual and lexical relations are then 
encoded between synsets of the same PoS: e.g., 
hyponymy, antonymy, meronymy, etc. (Miller et 
al. 1990; Fellbaum 1998b). 
Within the EuroWordNet (henceforth EWN) 
project I a similar (multilingual) lexical resource 
was developed, retaining the basic underlying 
design of WN, but enriching the set of lexical- 
semantic relations to be encoded for nouns and 
verbs in various ways 2, in order to obtain a 
maximally re-usable resource for computational 
applications. Thus, a) cross-PoS (xPos) relations 
were added so that different surface realizations 
of similar concepts within and across s 
could be matched (e.g., the noun research and 
the verb to research could be linked as 
1 EWN was a project in the EC Language 
Engineering (LE-4003 and LE-8328) programme. In 
a first phase, the partners involved were the 
University of Amsterdam (coordinator); the Istituto 
di Linguistica Computazionale, CNR, Pisa; the 
Fundacion Universidad Empresa (a cooperation of 
UNED, Madrid, Politecnica de Catalunya, Barcelona, 
and the University of Barcelona); the University of 
Sheffield; and Novell Linguistic Development 
(Antwerp), changed to Lemout & Hauspie during the 
project. In a further phase, the database was extended 
with German, French, Estonian and Czech. Complete 
information on EWN can be found at its web site: 
http://www.hum.uva.nl/~ewn/. 
2 Adjectives and adverbs were encoded in EWN only 
as targets of relations from nouns and verbs. 
&9 
  
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'XPOS NEAR__SYNONYMS' in EWN); b) some 
relations were identified which provide detailed 
information on semantic components lexicalized 
within word roots (e.g., to hammer could be 
linked to the noun hammer by means of an 
'INVOLVED_INSTRUMENT' relation); c) some 
labels were distinguished which could be added 
to relations to make their semantic entailments 
more explicit and precise (cf. Alonge et al. 
1998). The links among the wordnets of 
different s were realized by means of an 
Interlingual-Index (ILI), constituted by an 
unstructured list of the Princeton WN (version 
1.5) synsets. In addition, a hierarchy of 
-independent concepts, reflecting 
fundamental semantic distinctions (e.g., Object 
and Substance, Dynamic and Static, Cause, 
Manner, etc.), was built: the Top Ontology 
(TO). The TO consists of -independent 
features which may (or may not) be lexicalized 
in various ways, or according to different 
patterns, in different s (Rodriguez et al. 
1998). Via the ILI, all the concepts in the 
monolingual wordnets are directly or indirectly 
linked to the TO. The following picture shows 
the EWN data structure (see also Vossen 1999): 
While in WN all PoSs are represented, in EWN 
detailed information was encoded only for nouns 
and verbs and no analysis was carried out with 
respect to lexical-semantic relations which could 
be used to describe the semantics of adjectives 
and adverbs. In an Italian National Project we 
are building a large wordnet, ltalWordNet 
(henceforth IWN) 3, by extending the network 
built for Italian in EWN. Thus, we are both 
increasing the coverage for nouns and verbs and 
adding adjectives and some adverbs 4. To be able 
to encode information on adjectives we have 
enriched the set of the EWN lexical-semantic 
relations, aiming at encoding data which can be 
useful for computational applications. Moreover, 
we have revised the TO in order to account for 
the semantics of the new lexical categories being 
encoded. 
In this paper we describe the main changes made 
to the EWN framework in order to encode 
information on adjectives in IWN. Firstly, we 
provide a brief overview of the WN treatment of 
adjectives. Then, we discuss the set of relations 
being encoded for this category in IWN. Finally, 
we show the integration made to the EWN TO. 
We then conclude the paper by adding some 
remarks on the utility of the data being encoded 
for computational applications. 
1 Adjectives in WN 
In WN adjectives are divided into two major 
classes: descriptive adjectives and relational 
adjectives. A descriptive adjective is "one that 
ascribes a value of an attribute to a noun" 
(Fellbaum et al. 1993:27). Descriptive adjectives 
combine with nouns to express some qualities of 
the thing, person or concept they designate. 
Typically, in this group we find adjectives that 
designate the physical dimension of an object, 
its weight, abstract values etc. Besides these 
referent-modifying adjectives, we also find 
reference-modifying adjectives (cf. Bolinger 
1967; Chierchia & McConnel-Ginet 1990 name 
them intensional adjectives). Typical examples 
of the latter are former, future, present. 
3 ItalWordNet will be the reference lexical resource 
among various integrated  resources and 
software tools for the automatic treatment of the 
Italian written and spoken  which are being 
developed within the SI-TAL ('Integrated System for 
the Automatic Treatment of Language') National 
Project. 
4 Actually, we shall only encode adverbs derived 
from adjectives by adding the suffix -mente, for 
which a derivation relation with an adjective will be 
encoded. 
  
 43 
Relational adjectives, on the other hand, mean 
something like "relating/pertaining to, associated 
with", and usually have a morphologically 
strong link with a noun. Typical examples are 
musical, atomic, chemical 5. 
Synonymy is the basic relation encoded for all 
the PoSs (since it is used to build synsets). 
While in WN the noun and verb networks are 
then mainly developed around the superordinate 
(hyper/hyponymy) relationship, the organization 
of descriptive adjectives can "be visualized in 
terms of barbell-like structures, with a direct 
antonym in the centre of each disk surrounded 
by its semantically similar adjectives (which 
constitute the indirect antonyms of the adjectives 
in the opposed disk)" (Fellbaum 1998a: 212). 
The main relation encoded for these adjective 
synsets is antonymy, claimed to be the most 
prominent relation, both from a psycholinguistic 
point of view and from a more strictly lexical- 
semantic one, in the definition of the semantics 
of descriptive adjectives. Hyponymy is 
substituted by a 'similarity' relation. 
Relational adjectives, on the contrary, are not 
organized in this way, because their semantics 
cannot be described by using these relations. 
Indeed, they only point to the noun to which 
they pertain (e.g. atomic is linked to atom). 
Finally, information on the selectional 
preferences of both descriptive and relational 
adjectives is sometimes encoded (e.g., between 
high and degree), by using an 'is_attribute_of' 
relation. 
2 The IWN relations for adjectives 
As for the other lexical categories, also in IWN 
the basic relation encoded for adjectives is 
synonymy, on the basis of which synsets are 
built. Following EWN, we also encode a 
NEAR._SYNONYMY relation when two synsets are 
very close in meaning but their members cannot 
be put in the same synset (and no other relation 
results appropriate to link them; see Alonge et 
s Note that some adjectives have both a descriptive 
sense and a relational one. For example, musicale 
(musical) can modify the noun voce (voice) when we 
want to say that a voice is sweet-sounding and 
melodious, but can also be combined with the noun 
strumento (instrument) when we want to indicate that 
an instrument can be used to produce music. 
al. 1998 for a discussion of this relation, and 
Alonge et al., in prep., for a complete and 
detailed discussion of the linguistic design of 
IWN). Then, we encode a number of additional 
relations, which have been identified by taking 
into consideration i) theoretical works; ii) the 
EAGLES recommendations on semantic 
encoding (cf. Sanfilippo et al. 1999); iii) the data 
available in our sources; iv) possible use of data 
encoded in computational applications. 
2.1 Hyponymy 
Together with synonymy, the hyponymy relation 
constitutes the 'bone structure' of both WN and 
EWN. However, as we have seen, in WN the 
possibility of encoding hyponymy for adjectives 
is denied and the basic relation encoded for 
adjectives is antonymy, while EWN did not 
really deal with adjectives and a complete 
network for them was not built. 
Within IWN we have reconsidered the 
possibility of encoding hyponymy for adjectives. 
By analysing data coming from machine- 
readable dictionaries we find subsets of 
adjectives which have a genus + differentia 
definition, like nouns or verbs. That is, these 
adjectives seem to be organised into classes 
sharing a superordinate. This is the case, e.g., of 
adjectives indicating a 'containing' property 
(acquoso - watery; alcalino - alkaline), or a 
'suitable-for' property (difensivo - defensive; 
educativo - educational), etc. In IWN we have 
decided, therefore, to encode hyponymy also for 
these sets of adjectives. The taxonomies which 
can be built on the basis of this relation are 
different from those built for nouns or verbs, 
since they are generally very flat, consisting 
almost always of two levels only (an exception 
is the color adjectives taxonomy). However, by 
encoding a hyponymy relation for these 
adjectives, we obtain classes for which it will be 
possible to make various inferences. For 
instance, it will be possible to infer semantic 
preferences of certain classes: e.g., all the 
adjectives occurring in the taxonomy of 
contenente (containing) will occur as attributes 
of concrete nouns; adjectives found in the 
taxonomy of affetto (affected by an illness) will 
never be predicated of nouns referring to 
objects, etc. Furthermore, it will also be possible 
to infer information on syntactic characteristics 
/I/11 
  
 44 
of adjectives found in the same taxonomy: e.g., 
the hyponyrns of atto (suitable for) are always 
found in predicative position (and do not accept 
any complements); the hyponyms of privo 
(lacking) may occur both in attributive and in 
predicative position (and may take certain 
prepositional complements), etc. 
As it was done for all the relations identified in 
EWN, we have built substition tests or 
diagnostic frames based on normality 
judgements (cf. Cruse 1986). Inserting two 
words in the test sentences built evokes a 
'normality'/ 'abnormality' judgement on the 
basis of which each relation can be determined. 
These tests are used by encoders both to verify 
the existence of relations between synsets and to 
encode them in a consistent way (for a complete 
lists of the tests built see Alonge et al., in prep.) 
2.2 Antonymy 
As in WN, also in IWN the antonymy relation 
remains an important relation to describe the 
semantics of various adjectives. Following 
theoretical work (Lyons 1977; Cruse 1986), we 
have further distinguished between 
COMPLEMENTARY ANTONYMY and GRADABLE 
ANTONYMY 6. The former relation links 
adjectives referring to opposing properties/ 
concepts: when one holds the other is excluded 
(alive~dead). The latter relation is used for those 
antonym pairs which refer to gradable properties 
(long~short). In case it is not clear if two 
opposing adjectives refer to complementary or 
gradable properties, we can still use an 
underspecified ANTONYMY relation. Also this 
information can be useful for computational 
applications since word pairs presenting one of 
the two kinds of opposition may occur in 
different contexts (cf. Cruse 1986). 
2.3 Other relations 
In WN a relation between adjectives and nouns 
is encoded for relational adjectives which point 
to a noun to which they 'pertain': atomic~atom, 
industrial~industry, etc. This relation will be 
encoded also in IWN, by using the label 
PERTAINS TO. 
Another relation 'inherited' from WN can be 
useful to distinguish both adjective senses and 
their semantic preferences7: 
altol (tall) IS A VALUE OF statura (stature) 
alto2 (high) IS_A_VALUEOF altezza (height). 
Other relations are then being encoded which are 
not in WN, but are encoded for nouns and verbs 
in EWN. In WN each PoS forms a separate 
system of -internal relations and 
conceptually close concepts are totally separated 
only because they differ in PoS. In EWN, instead 
of using as main classificatory criterion the 
traditional distinction among PoSs, drawn upon 
heterogeneous criteria, a purely semantic 
distinction was adopted (following Lyons 1977). 
Thus, a distinction was drawn among I st order 
entities (loes - referred to by concrete nouns), 
2 "d order entities (2oes - referred to by verbs, 
adjectives or nouns indicating properties, states, 
processes or events), and 3 rd order entities (3oes 
referred to by abstract nouns indicating 
propositions existing independently of time and 
space). By drawing this distinction it was 
possible to relate lexical items that, either within 
a  or across different s, refer to 
close concepts, although they belong to different 
PoSs. Thus, as said above, the possibility to 
encode 'near-synonymy' between synsets of the 
same order (but different PoSs) was provided. 
Furthermore, other cross-PoS relations were 
identified which allow to obtain a better 
description of word meanings. In IWN we 
maintain the same distinction among semantic 
orders and encode for adjectives some relations 
which can be encoded for the other 2oes. In 
particular, we encode the 'INVOLVED' and 
'CAUSE' relations. 
The INVOLVED relation links a 2oes with aloe 
or 3oe referring to a concept incorporated within 
6 A similar distinction is also made within the 
SIMPLE EC project (LE-8346), whose goal is adding 
semantic information to the set of harmonized 
lexicons built within the PAROLE project for twelve 
European s. Of course, the sub-classification 
ofantonymy can also be used for nouns and verbs. 
7 Furthermore, these relations being encoded between 
an adjectival synset and a nominal or verbal one are 
also useful to distinguish adjective classes as 
described by Dixon (1991), and reported in 
Sanfilippo et al. (1999). Indeed, such classes are 
often indicated by the nouns linked to adjectives. 
45 
  
 45 
the meaning of the 2oe 8. Examples for adjectives 
are given in the following: 
filoso (= pieno di fili) (thready, filamentous) 
HAS_HYPERONYM pieno (full) 
INVOLVED filo (thread) 
imberbe (= privo di barba) (beardless) 
HAS_HYPERONYM privo (lacking) 
INVOLVED barba (beard). 
Another relation which can be encoded is the 
CAUSE relation: 
depuratorio (= atto a depurare) (depurative, 
purifying) 
HAS_HYPERONYM atto, adatto 
(suitable for) 
CAUSES non-factive 9 depurare 
(to purify) 
compensativo (= che serve a compensare) 
(compensatory) 
HAS._HYPERONYM alto, adatto 
(suitable for) 
CAUSES non-factive compensare 
(to compensate) 
A new relation, not present either in WN or in 
EWN, will be encoded for a class of adjectives 
indicating the possibility of some events 
occurring: 
giudicabile (= che pub essere giudicato) (triable) 
LIABLE_TO giudicare (to judge) 
inaccostabile, inavvicinabile (= che non pub essere 
avvicinato) (which cannot be approached) 
LIABLETO awicinare, accostare 
(to approach) negative. 
8 E.g., to lapidate has as INVOLVED_INSTRUMENT 
stone; to work has as INVOLVED_AGENT worker (see 
Alonge et al. 1998). 
9 As said above, in EWN various features were 
encoded to make implications of relations explicit: 
conjunction and disjunction (for multiple relations of 
the same kind encoded for a synset); non-factivity (to 
indicate that a causal relation does not necessarily 
apply); intention (added to a cause relation to indicate 
intention to cause a certain result); negation (to 
explicitly encode the impossibility of a relation 
occurring). These features are also used in IWN. 
The table below gives an overview of the main 
relations being encoded for adjectives in IWN 
(for the other relations being encoded for 
adjectives see Alonge et al., in prep.): 
GRAD ANTONYMY 
COMP ANTONYMY 
HYPONYMY 
PERTAINS TO 
IS A VALUE_OF 
INVOLVED 
CAUSE 
LIABLE_TO 
adj/adj 
adj/adj 
adj/adj 
adj/noun 
adj/noun 
adj/noun 
adj/verb; 
adj/noun 
adj/verb; 
adj/noun 
beautiful/u~ly 
alive/dead 
watery/containing 
chemical/chemistry 
tail/stature 
dental/tooth 
depurative/ 
to depurate 
triable/to judge 
3 The IWN Top Ontology 
In the EWN TO all the entities belonging to the 
2 nd order have been organized into two different 
classification schemes, which represent the first 
division below 2 nd Order Entity: 
• Situation Type: the event-structure or 
Aktionsart (or lexical aspect) of a situation; 
• Situation Components: the most salient 
semantic components that characterize 
situations. 
The Situation Types provide a classification of 
2oes in terms of the event-structure (or 
Aktionsart) of the situation they refer to: a basic 
distinction was drawn between Static and 
Dynamic. The Situation Components represent a 
more conceptual and intuitive classification of 
word meanings because they can be viewed like 
the most salient semantic components of a 
concept. Examples of Situation Components 
are: Manner, Existence, Communication, Cause. 
Situation Type represents disjoint features that 
cannot be combined, whereas it is possible to 
assign any combination of Situation 
Components to a word meaning. Here below the 
Top Concepts identified for 2oes are shown: 
46 
  
 46 
2 ND ORDER ENTITY 
SITUATION COMPONENT 
Cause 
Communication 
Condition 
Existence 
Experience 
Location 
Manner 
Mental 
Modal 
Physical 
Possession 
Purpose 
Quantity 
Social 
Time 
SITUATION TYPE 
Dynamic 
BoundedEvent 
UnboundedEvent 
Static 
Property 
Relation 
In order to be able to draw generalizations on 
adjective meanings by using the TO, we 
partially modified this scheme. First of all, we 
moved the PROPERTY and RELATION nodes 
under the SITUATION COMPONENT node. This 
was done for two interconnected reasons: first of 
all, because this distinction is not directly linked 
to Aktionsart (lexical aspect), while the 
distinctions under SITUATION TYPE are 
Aktionsart distinctions, i.e. they are connected 
with the "the procedural characteristics (i.e. the 
'phasal structure', 'time extension' and 'manner 
of development') ascribed to any given situation 
referred to by a verb phrase" (Bache 1982: 70) ~°. 
Secondly, adjectives may refer to PROPERTIES or 
RELATIONS, but they may be either stative or not 
(cfr. e.g. Lakoff 1966; Quirk et al. 1985; Peters 
et al. 1999). Thus, in our system it has to be 
possible to specify that an adjective expresses a 
PROPERTY while being DYNAMIC. In any case, 
since many adjectives may have both a 
DYNAMIC sense and a STATIC one, we have also 
the possibility to under-specify this information 
Io Of course, in EWN all 2oes (and therefore also 
nouns or adjectives) can be classified according to 
their Aktionsart. 
by linking adjectives directly to the SITUATION 
TYPE node. 
Adjectives may indicate many different types of 
properties: temporal (passeggiata mattutina - 
morning walk), psychological (canzone triste - 
sad song), social (uomo ricco - rich man), 
physical (superficie legnosa - wooden surface), 
physiological (bambino magro - thin child), 
perceptive (minestra calda hot soup), 
quantitative (magra ricompensa - poor reward) 
and intensity properties (vino forte - strong 
wine). In the EWN TO there are already nodes 
which may be used to represent these 
distinctions (TIME, MENTAL, SOCIAL, PHYSICAL, 
QUANTITY) but we needed to better specify or 
also add some features. For example, we have 
added, under the already present node 
PHYSICAL, the node MATERIAL, to represent, 
among others, some Italian adjectives ending in 
-oso (for example legnoso - wooden, acquoso - 
watery) which indicate the property of 
containing a certain material. Moreover, we 
added the node PHYSIOLOGICAL (to classify 
adjectives corresponding to tired, hungry, sick, 
etc.) under PHYSICAL. For adjectives denoting an 
intensity, we then added the node INTENSITY 
directly under the SITUATION COMPONENT node. 
One of the main problem we had was that no 
Top Concept in the EWN TO could be used to 
classify the reference-modifying adjectives (cf. 
above). These are a very particular kind of 
adjectives, because they do not indicate a 
property of the referent of the noun they modify. 
So, aiming at showing the distinction between 
referent-modifiers and reference-modifiers, we 
created two new Top Concepts under the node 
PROPERTY: ATTRIBUTE and FUNCTIONAL, where 
the latter can be used for reference-modifying 
adjectives (according to the definition provided 
by Chierchia & McConnel-Ginet 1990 for the 
category referred to by these adjectives: "a 
function from properties to properties"). 
Like all descriptive adjectives, also the 
reference-modifiers classified under the node 
FUNCTIONAL can be linked to other SITUATION 
COMPONENTS. Functional adjectives for which 
the temporal aspect prevails (ex former, 
presente - present) can be classified under the 
node TIME; adjectives referring to some 
'epistemological' property (potenziale 
potential, necessario - necessary) can be linked 
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 47 
tO MODAL 11; etc. A particular case of functional 
adjectives are the 'argumental' ones. They 
introduce a comparison between different 
entities (e.g., simile - similar, diverso - different, 
etc.). A comparison presupposes a relation 
between different entities so these adjectives can 
be linked to both PROPERTY and RELATION. 
Since in the EWN TO these two Top Concepts 
were two different kinds of SITUATION TYPE, 
they were mutually exclusive; now, in the IWN 
revised TO they can be conjoined. 
Here below the IWN Top Concepts for 2 "a Order 
Entities are shown: 
2 N° ORDER ENTITY 
SITUATION COMPONENT 
Cause 
Communication 
Condition 
Existence 
Experience 
Location 
Manner 
Mental 
Modal 
Physical 
Material 
Physiological 
Possession 
Purpose 
Quantity 
Social 
Time 
Intensity 
Property 
Attribute 
Functional 
Relation 
SITUATION TYPE 
Dynamic 
BoundedEvent 
UnboundedEvent 
Static 
Concluding remarks 
Although the ANTONYMY, PERTAINS_TO, and 
ISATTRIBUTEOF relations, already encoded in 
the Princeton WN, are fundamental relations to 
describe the adjective semantics, and they can be 
11 Since this node is used for situations involving the 
possibility or likelihood of other situations. 
useful for computational applications which 
exploit our resource, we believe that the 'new' 
relations being encoded may provide equally 
relevant information, especially because many 
adjectives cannot be defined by means of the 
WN relations. Let's take into consideration just 
a few examples. 
The adjective depresso is ambiguous in that it 
has (at least) three readings: 
1) which has been lowered, flattened (said of a 
land); 
2) being in bad physical or moral conditions (said 
of an area, a country); 
3) affected by depression (said of a person). 
For these three senses of the adjective we would 
encode different relations, extractable from our 
sources: 
depressol IS_CAUSEDBY deprimere (to lower) 
IS A VALUE_OF terreno (land) 
depresso2 
depress% 
HAS_HYPERONYM colpito (affected) 
IS_CAUSEDBY depressionel 
(depression - economic sense) 
HAS_HYPERONYM affetto 
(affected, suffering from sthg.) 
IS_CAUSED_BY depressione2 
(depression - medical sense) 
The relations encoded could, e.g., help 
disambiguate the occurrences of the adjective in 
contexts such as: Gianni era depresso (Gianni 
was depressed) or Quella regione ~ depressa 
(That area is depressed). Indeed, by checking the 
semantic information encoded for the two senses 
of depressione linked to depresso, it's possible 
to provide the right interpretation for the 
sentences under analysis; on the other hand, the 
first sense of the adjective would be excluded 
because of the IS A VALUE OF relation 
encoded. In this case, also the TO links could be 
helpful: actually, depresso2 would be linked to 
SOCIAL and CONDITION, while depresso3 would 
be linked to MENTAL and EXPERIENCE. 
Many adjectives found in our sources do not 
seem to have (lexicalized) antonyms, nor cannot 
be defined by using the PERTAINS_TO or 
IS A VALUE OF relations. These are often 
linked to nouns or verbs, by various relations: 
/IQ 
  
 48 
piumato = coperto di piume (plumed = covered with 
plumage) 
HAS HYPERONYM coperto 
INVOLVED piuma 
distillabile =che pub essere distillato (distillable = 
which can be distilled) 
LIABLE TO distillare. 
For these and many other adjectives the 'new' 
relations identified in IWN are necessary, given 
that we often cannot encode other relations for 
them. The relations encoded provide 
fundamental semantic information on them, 
which can, for instance, be used to infer 
semantic preferences (e.g., only certain loes can 
be modified by piumato, etc.). The inclusion of 
this information in a large database which is 
mainly intended for computational applications 
can be very useful, mainly because it may help 
in resolving ambiguities and may be used to 
draw inferences of different nature. 

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