COMPUTER AIDED INTERPRETATION OF LEXICAL COOCCURRENCES 
Paola Velardi (*) 
Mafia Teresa Pazienza (**) 
(*)University of Ancona, Istituto di Informatica, via Brecce Bianche, Ancona 
(**)University of Roma, Dip. di lnformatica e Sistemistica, via Buonarroti 12, Roma 
ABSTRACT 
This paper addresses the problem of developing a 
large semantic lexicon for natural language 
processing. The increas~g availability of machine 
readable documents offers an opportunity to the 
field of lexieal semantics, by providing experimental 
evidence of word uses (on-line texts) and word 
definitions (on-line dictionaries). 
The system presented hereafter, PETRARCA, 
detects word e.occurrences from a large sample of 
press agency releases on finance and economics, 
and uses these associations to build a ease-based 
semantic lexicon. Syntactically valid cooccurenees 
including a new word W are detected by a 
high-coverage morphosyntactic analyzer. Syntactic 
relations are interpreted e,g. replaced by case 
relations, using a a catalogue of 
patterns/interpretation pairs, a concept type 
hierarchy, and a set of selectional restriction rules 
on semantic interpretation types. 
Introduction 
Semantic knowledge codification for language 
processing requires two important issues to be 
considered: 
1. Meaning representation. Each word is a world: 
how can we conveniently circumscribe the 
semantic information associated to a lexic,;d 
entry? 
2. Acquisition. For a language processor, to 
implement a useful application, several 
thousands of terms must have an entry in the 
semantic lexicon: how do we cope with one 
such a prohibitive task? 
185 
The problem of meaning representation is one 
which preoccupied scientists of different disciplines 
since the early history of human culture. We will 
not attempt an overall survey of the field of 
semantics, that provided material for many 
fascinating books; rather, we will concentrate On 
the computer science perspective, i.e. how do we 
go about representing language expressions on a 
computer, in a way that can be useful for natural 
language processing applications, e.g. machine 
translation, information retrieval, user-friendly 
interfaces. 
In the field of computational linguistics, several 
approaches were followed for representing semantic 
knowledge. We are not concerned here with 
semantic languages, which are relatively well 
developed; the diversity lies in the meaning 
representation principles. We will classify the 
methods of meaning representations in two 
categories: conceptual (or deep) and coilocative (or 
surface). The terms "conceptual" and "collocative" 
have been introduced in \[81; we decided to adopt an 
existing terminology, even though our 
interpretation of the above two categories is 
broader than for their inventor. 
1. Conceptual Meaning Conceptual meaning is the 
cognitive content of words; it can be expressed 
by features or by primitives. Conceptual 
meaning is "deep" in that it expresses 
phenomena that are deeply embedded in 
language. 
2. Collocatlve meaning. What is communicated 
through associations between words or word 
classes. Coilocative meaning is "superficial" in 
that does not seek for "the deep sense" of a 
word, but rather it "describes" its uses in 
everyday language, or in some sub-w, rid 
language (economy, computers, etc.). It 
provides more than a simple analysis of 
cooccurr~aces, because it attempts an 
explanation of word associations in terms of 
conceptual relations between a lexical item and 
other items or classes. 
Both conceptual and collocative meaning 
representations are based on some subjective, 
human-produced set of primitives (features, 
conceptual dependencies, relations, type hierarchies 
etc.) on which there is no shared agreement at the 
current state of the art. As far as conceptual 
meaning is concerned, the quality and quantity of 
phenomena to be shown in a representation is 
subjective as well. On the contrary, surface meaning 
can rely on the solid evidence represented by word 
associations; the interpretation of an association is 
subjective, but valid associations arc an observable, 
even though vast, phenomenon. To confu'm this, 
one can notice that different implementations of 
lexicons based on surface meaning are 
surprisingly similar, whereas conceptual lexicons arc 
very dishomogeneous. 
In principle, the inferential power of collocative, or 
surface \[18\] meaning representation is lower than 
for conceptual meaning. In our previous work on 
semantic knowledge representation, however, \[10l 
\[18\] \[12\] we showed that a semantic dictionary in 
the style of surface meaning is a useful basis for 
semantic interpretation. 
The knowledge power provided by the semantic 
lexicon (limited to about I000 manually entered 
defmitions) was measured by the capability of the 
language processor DANTE \[2\] \[18\] \[11\] to answer 
a variety of questions concerning previously 
analyzed sentences (press agency releases on finance 
and economics). It was found that, even though 
the system was unable to perform complex 
inferences, it could successfully answer more than 
90% of the questions \[12\]L In other terms, surface 
semantics seems to capture what, at first glance, a 
human reader understands of a piece of text. 
In\[26\] , the usefulness of this meaning 
representation method is demonstrated for 
TRANSALTOR, a system used for machine 
translation in the field of computers. 
An important advantage of surface meaning is that 
makes it easier the acquisition of the semantic 
lexicon. This issue is examined in the next section. 
Acquisition of Lexical Semantic 
Knowledge. 
Acquiring semantic knowledge on a systematic 
basis is quite a complex task. One needs not to 
look at metaphors or idioms to fred this; even the 
interpretation of apparently simple sentences is 
riddled with such difficulties that makes it hard 
even cutting out a piece of the problem. A manual 
codification of the lexicon is a prohibitive task, 
regardless of the framework adopted for semantic 
knowledge representation; even when a large team 
of knowledge enters is available, consistency and 
completeness are a major problem. We believe 
-that automatic, or semi-automatic acquisition of 
the lexicon is a critical factor in determining how 
widespread the use of natural language processors 
will be in the next few years. ' 
Recently a few methods were presented for 
computer aided semantic knowledge acquisition. A 
widely used approach is accessing on-line dictionary 
defmitions to solve ambiguity problems \[3\] or to 
derive type hierarchies and semantic features \[24\]. 
The information presented in a standard dictionary 
has in our view some intrinsic limitation: 
s definitions are often circular e.g. the definition 
of a term A may refer to a term B that in turn 
points to A; 
* definitions are not homogeneous as far as the 
quality and quantity of provided information: 
they can be very sketchy, or give detailed 
structural information, or list examples of 
use-types, or attempt some conceptual meaning 
definition; 
• a dictionary is the result of a conceptualization 
effort performed by some human specialist(s); 
this effort may not be consistent with, or 
The test was performed over a 6 month period on about S0 occasional visitors and staff members of the 
IBM Rome scientific center, unaware of the system capabilities and structure. The user would look at 60 
different releases, previously analyzed by the system (or re-analyzed during the demo), and freely asks 
questions about the content of these texts. In the last few months, the test was extended to a different 
domain, e.g. the Italian Constitution, without significant performance changes. See the referenced papers for 
examples of sentences and of (answered and not answered) query types (in general wh-questions). 
186 
exl (from \[8\]): 
boy = + artimate -adult + male 
ex2. (from \[251): 
help = 
Y carrying out Z, X uses his resources W in order for W to help 
Y to carry out Z; the use of resources by X and the carrying out of Z 
by Y are simultaneous 
ex2 (from I161): 
throw = 
actor PROPELs and object from a source LOCation to a 
destination LOCation 
Figure I. 
suitable for, the objectives of an application for 
which a language processor is built. 
Examples of conceptual meaning representation in the literature 
A second approach is using corpora rather than 
human-oriented dictionary entries. Corpora provide 
an experimental evidence of word uses, word 
associations, and language phenomena as 
metaphors, idioms; and metonymies. 
The problem and at the same time the advantage of 
corpora is that they are raw texts whereas 
dictionary entries use some formal notation that 
facilitates the task of linguistic data processing. 
No computer program may ever be able to derive 
formatted data from a completely unformatted 
source. Hence the ability of extracting lexical 
semantic information form a corpus depends upon 
a powerful set of mapping rules between phrasal 
patterns and human-produced semantic primitives 
and relations. We do not believe that a semantic 
representation framework is "good" if it mimics a 
human cognitive model; more realistically, we 
believe that a set of primitives, relations and 
mapping rules is "fair', when its coverage over a 
language subworld is suitable for the purpose of 
some useful language processing activity. Corpora 
represent an 'objective" description of that 
subworld, against which it is possible to evaluate 
the power of a representation scheme; and they are 
particularly suitable for the acquisition of a 
colloeative meaning based semantic lexicon. 
Besides our work \[19\], the only knowledge 
acquisition system based on corpora (as far as we 
know) is described in \[7\]. In this work, when an 
unknown word is encountered, the system uses 
pre-existing knowledge on the context in which the 
word occurred to derive its conceptual category. 
187 
The context is provided by on line texts in the 
economic domain. For example, the unknown 
word merger in "another merger offer" is 
categorized as merger-transaction using semantic 
knowledge on the word offer and on pre-analyzed 
sentences referring to a previous offer event, as 
suggested by the word another. This method is 
interesting but reties upon a pre-existing semantic 
lexicon and contextual knowledge; in our work, the 
only pre-existing knowledge is the set of conceptual 
relations and primitives. 
PETRARCA: a method for the 
acquisition and interpretation of 
cooccurrences 
PETRARCA detects cooccurrences using a 
powerful morphologic and syntactic anal~er \[141 
I11; cooccurences are interpreted by a set of 
phrasal-patterns/ semantic-interpretation mapping 
rules. The semantic language is Conceptual Graphs 
\[17\]; the adopted type hierarchy and conceptual 
relations are described in \[10l. The following is a 
summary description of the algorithm: 
For any word W, 
1. (A) Parse every sentence in the corpus that 
uses W. 
Ex: W = AGREEMENT 
"Yesterday an agreement was reached among 
the companies". 
exl (from I181): 
agreement = 
is a decision act 
participant pe-rson, organization 
theme transaction 
cause communication_exchange 
manner interesting important effective .. 
ex2 (from \[26\]): 
person = 
/sa creature 
agent_of take put fred speech-action mental-action 
consistof hand foot.. 
source_of speech-action 
destination_of speech-action 
power human 
speed slow 
mass human 
Figure 2. Examples of eollocative meaning representation in the literature 
2. (A) Determine all syntactic attachments of W * 
(e.g. syntactically valid cooccurrences) Ex: 
. 
NP_PP(AGREEMENT,AMONG,COMPANY). 
VP_OBJ(TO REACH,AGREEMENT). 
(A) Generate a semantic interpretation for 
each attachment : 
step 3 might produce more than one 
interpretation for a single word pattern, due to 
the low selectivity of some semantic rule. 
step 3 might fail to produce an interpretation 
for metonymies and idioms, which violate 
semantic constraints. Strong syntactic evidence 
(unambiguous syntactic rules) is used to 
"signal" the user this type of failure. 
Ex: Knowledge sources used by PETRARCA 
IAGREEMENT}- • (PARTICIPANT)- • ICOMPANYi. 
4. (A) Generalize the interpretations. 
Ex: Given the following examples: 
\[AGREEMENT l- • (PARTICIPANT)- > ICOMPANYI. 
\[AGREEMENT\]- > (PARTICIPANT)- • \[COUNTRY.ORGANIZATIONI. 
\[AGREEMENT}- • (PARTICIPANT)- • \[PRESIDENT I. 
derive the most general constraint: 
\[AGREEMENT\]- • (PARTICIPANT)- > IHUMAN.ENTITYI. The 
above is a new case description added to the 
definition of AGREEMENT 
5. (M) Check the newly derived entry. 
To perform its analysis, PETRARCA uses five 
knowledge sources: 
I. an on line natural corpus (press agency 
releases) to select a variety of language 
expressions including a new word W; 
2. a high coverage morphosyntactic analyzer, to 
derive phrasal patterns centered around W; 
3. a catalogue of patterns/interpretation pairs, 
called Syntax-to-Semantic (SS rules); 
4. a set of rules expressing selectional restriction 
on conceptual relation uses (CR rules); 
5. a hierarchy of conceptual classes and a 
catalogue associating to words concept types. 
Steps marked (A) are automatic; steps marked (M) 
axe manual. The only manual step is the last one: 
this step is however necessary because of the 
following: 
The natural corpus and the parser are used in steps 
1 and 2 of the above algorithm; SS rules, CR rules 
and the word/concept catalogue are used in step 3; 
the type hierarchy is used in steps 3 and 4 
188 
The parser used by PETRARCA is a high coverage 
morphosyntactic analyzer developed in the context 
of the DANTE system. The lexical parser is based 
on a Context Free grammar, the complete set of 
Italian prefixes and suffixes, and a lexicon of 7000 
elementary lernmata (stems without affixes). At 
present, the morphologic component has an 100% 
coverage over the analyzed corpus (100,000 words) 
1141 1131. 
The syntactic analysis determines syntactic 
attachment between words by verifying grammar 
rules and forms agreement; the system is based on 
an Attribute Grammar, augmented with lookahead 
sets I1\]; the coverage is about 80%; when compiled, 
the parsing time is around 1-2 see. of CPU time for 
a sentence with 3-4 prepositional phrases; the CPU 
is an IBM mainframe. 
The syntactic relations detected by the parser are 
associated to possible semantic interpretations using 
SS rules. An excerpt of SS rules is given below for 
the phrasal pattern: 
noun..phrase( NP) + prepositional..phrase( PP) 
(di=o.D. 
i NP PP('wordl,d|."word2) •- tel(PO.f~E$S,di°'word2,*lmrdl). 
l'clne dl Pletro (the do s of Peter)'/ 
NP_PP('wordl,dl,'word2) <. reI(.SOC RELATION,dl,'word2,'wordl). 
/'lit mtdre rq Elet,o (the mitther of Peter)'/ 
NP PP('wm'dhdi,'word2) < • rei(PART1CIPANT,di,*wofdl,'word2). 
/'riunione dei deleptl (the meeting of the delesliel)'/ 
NP PP('wocdl.di.'word2) <- rel($UBSET0dt.'wocd2.'wordl). 
/'due d! nol (two of us)'/ 
NP_PP('wo~I,di.'word2) < - mI(PART OF.di.'wortl2,'wordl). 
/'p=glne del Itbro (the pitgel of the book)'/ 
NP_PP('wonll.dl.'word2) •. ml(MATTER.dl,'wordl.'word2). 
I'oglFtto dl legno (itn object of wood)'/ 
NP_PP('wordl,dl,'word3) < - rel(PRODUeER,di,'wordl,*word2). 
/'rul~ito del leonl (the rmlr of the lions)'/ 
NP_PP("~mrdl,dl,'wottl '2) <- reI(CHARACTERISTIC.d.I,'word2.'wordl). 
/'rintelllgenza delrtlomo (the intelligence of the man)'/ 
Overall, we adopted about 50 conceptual relations 
to describe the set of semantic relations commonly 
found in language; see \[10\] for a complete list. The 
catalogue of SS rules includes about 200 pairs. 
Given a phrasal pattern produced by the syntactic 
parser, SS rules select a first set of conceptual 
relations that are candidate interpretations for the 
pattern. 
Selectional restriction rules on conceptual relations 
are used to select a unique interpretation, when 
possible. Writing CR rules was a very complex 
task, that required a process of progressive 
refinement based on the observation of the results. 
The following is an example of CR rule for the 
conceptual relation PARTICIPANT: 
participant -- 
189 
has..participant: meeting, agreement, fly, sail 
is.participant: human_entity 
Examples of phrasal patterns interpreted by the 
participant relation are: 
John flies (to New York); the meeting among 
parties; the march of the pacifists," a contract 
between Fiat and A lfa; the assembly of the 
administrators, etc. 
An interesting result of the above algorithm is the 
following: in general, syntax will also accept 
semantically invalid cooccurrences. In addition, in 
step 3, ambiguous words can be replaced by the 
"wrong" concept names. Despite this, selectional 
restrictions are able to interpret only valid 
associations and reject the others. For example, 
consider the sentence: "The party decided a new 
strategy". The syntax detects the association 
SUBJ(DECIDE, PARTY). Now, the word "party" 
has two concept names associated with it: 
POL PARTY, and FEAST, hence in step 3 both 
interpretations are examined. I lowever, no 
conceptual relation is found to interpret the pattern 
"FEAST DECIDE". This association is hence 
rejected. 
Simalirily, in the sentence: "An agreement is 
reached among the companies, the syntactic 
analyzer will submit to the semantic interpreter two 
associations: 
NP_PP(A GREEMENT, AMONG, COMPA N Y) and 
VP_PP(REACIt, AMONG,COMPANY) Now, 
the preposition among in the SS rules, points to 
such conceptual relations as PARTICIPANT, 
SUBSET (e.g. "two among all us"), and 
LOCATION (e.g. "a pine among the trees'% but 
none of the above relates a MOVE ACT with a 
IIUMAN ORGANIZATION. The association is m 
hence rejected. 
Future experimentation issues 
This section highlights the current limitations and 
experimentation issues with PETRARCA. 
Definition of type hierarchies 
PETRARCA gets as input not only the word W, 
but a list of concept labels CWi, corresponding to 
the possible senses of W. For each of these CWi, 
the supertype in the hierarchy must be provided. 
Notice .however that the system knows nothing 
about conceptual classes; the hierarchy is only an 
ordered set of labels. 
In order to assign a supertype to a concept, three 
methods are currently being investigated. First, a 
program may "guide" the user towards the choice of 
the appropriate supertype, visiting top down the 
hierarchy. This approach is similar to the one 
described in I261. 
Alternatively, the user may give a fist of 
synonymous or near synonymous words. If one of 
these was already included in the hierarchy, the 
same supertype is proposed to the user. 
A third method lets the system propose the 
supertype. The system assumes CW=W and 
proceeds through steps 1, 2 and 3 of the case 
descriptions derivation procedure. As the supertype 
of CW is unknown, CR rules are less effective at 
determining a unique interpretation of syntactic 
patterns. If in some of these patterns the partner 
word is already defined in the dictionary, its case 
descriptions can be used to restrict the analysis. 
For example, suppose that the word president is 
unknown in: 
The president nominated etc. 
Pertini was a good president' 
the knowledge on possible AGENTs for 
NOMINATE let us infer 
PRESIDENT < HUMANENTITY; from the 
second sentence, it is possible to further restrict to: 
PRESIDENT< HUMAN ROLE. The third 
m 
method is interesting because it is automatic, 
however it has some drawbacks. For example, it is 
slow as compared 1:o methods 1 and 2; a trained 
user would rather use his experience to decide a 
supertype. Secondly, if the word is found with 
different meanings in the sample sentences, the 
system might never get to a consistent solution. 
Finally, if the database includes very few or vague 
examples, the answer may be useless (e.g. ACT, or 
TOP). It should also be considered that the effort 
required to assign a supertype to, say, 10.000 words 
is comparable with the encoding of the 
morphologic lexicon. This latter required about one 
month of data entry by 5-6 part-time researchers, 
plus about 2-3 months for an extensive testing. 
The complexity of hierarchically organizing 
concepts however, is not circumscribed to the time 
consumed in associating a type label to some 
thousand words. All NLP researchers 
experimented the difficulty of associating concept 
190 
types to words in a consistent way. Despite the 
efforts, no commonly accepted hierarchies have 
been proposed so far. In our view, there is no 
evidence in humans of primitive conceptual 
categories, except for a few categories as animacy, 
time, etc. We should perhaps accept the very fact 
that type hierarchies are a computer method to be 
used in NLP systems for representing semantic 
knowledge in a more compact form. Accordingly, 
we are starting a research on semi-automatic word 
clustering (in some given language subworld 
described by a natural corpus), based on fuzzy set 
and conceptual clustering theories. 
Interpretation of idiomatic expressions 
In the current version of PETRARCA, in case of 
idiomatic expressions the user must provide the 
correct interpretation. In case of metaphors, 
syntactic evidence is used to detect a metaphor, 
under the hypothesis that input sentences to the 
system are syntactically and semantically correct. 
At the current state of implementation, the system 
does not provide automatic interpretation of 
metaphors. However, an interesting method was 
proposed in 1201. According to this method, when 
for example a pattern such as "car drinks" is 
detected, the system uses knowledge of canonical 
definitions of the concepts "DRINK" and "CAR" 
to establish whether ~CAR" is used metaplaorically 
as a HUMANENTITY, or "DRINK" is used 
metaphorically as 1"O BE FEDBY". An 
interesting user aided computer program for 
idiomatic expressions analysis is also described in 
1231. 
Generalization of case descriptions 
In PERTRARCA, phrasal patterns are first 
mapped into 'low level" case description; in step 4, 
"similar" patterns are merged into "high level' case 
descriptions. In a first implementation, two or 
three low level case descriptions had to be derived 
before creating a more general semantic rule. This 
approach is biased by the availability of example 
sentences. A word often occurs in dozens of 
different contexts, and only occasionally two 
phrasal patterns reflect the same semantic relation. 
For example, consider the sentences: 
The company signs a contract for newfimding 
The ACE stipulates a contract to increase its influence 
Restricting ourselves to the word "contract', we get 
the following semantic interpretations of syntactic 
patterns: 
14SIGNI, > frHBlmtl~ > l¢Ol~Crl 
2.1COl~t~-~r}. ~ ll~ll~l~- • ll~l~llqO-'l 
Ms'rII~JI.&TIBI- > crI-IIBMII). > l¢OlCraAc~rl 
4.\[CONTRA~WI- > (PIJRPOSli). • ll~lll 
In patterns 1 and 3 "sign" and "stipulate" belong to 
the same supertype, i.e. 
INFORMATIONEXCHANGE; hence a new 
case description can be tentatively created for 
CONTRACT: 
ICOl,¢rr~cl+.l. • (TI'llIMI~. > IlI,+F'ORMA'rioI,,I+BXO.IA I~F. ! 
Indeed, one can tell, talk about, describe etc. a 
contract. 
Conversely, patterns 3 and 4 have no common 
supertype; hence two "low level" case descriptions 
are added to the definition of CONTRACT. 
lCONTRAC'rl. • (PURPOSE)- ~ ILmlJNDINGI 
ICOiCTRACI"I- > (PURPOSE)- • lll'~'ll, ltt.,~IIl 
Even with a large number of input sentences, the 
system createsmany of these specific patterns; a 
human user must review the results and provide for 
case descriptions generalization when he/she feels 
this being reasonable. 
A second approach is to generalize on the basis of 
a single example, and then retract (split) the rule if 
a counterexample is found. Currently, we axe 
~a'udying different policies and comparing the 
results; one interesting issue is the exploitation of 
counterexamples. 
Concluding remarks 
Even though PETRARCA is still an experiment 
and has many unsolved issues, it is, to our 
knowledge, the first reported system for extensive 
semantic knowledge acquisition. There is room for 
many improvements; for example, PETRARCA 
only detects, but does not interpret idioms; neither 
it knows what to do with errors; if a wrong 
interpretation of a phrasal pattern is derived, error 
correction and refinement of the knowledge base is 
performed by the programmer. However 
PETRARCA is able to process automatically raw 
language expressions and to perform a first 
191 
classification and encoding of these data. The rich 
linguistic material produced by PETRARCA 
provides a basis for future analysis and refinements. 
Despite its limitations, we believe this method 
being a first, useful step towards a more complete 
system of language learning. 
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