CONVERTING LARGE ON-LINE VALENCY DICTIONARIES 
FOR NLP APPLICATIONS: 
FROM PROTON DESCRIPTIONS TO METAL FRAMES 
GEERT ADRIAENS \[1,2\] GERT DE BRAEKELEER \[1\] 
\[1\] Siemens-Nixdorf Software Center Liege 
Rue Des Fories 2, 4020 Liege, Belgium 
\[2\] Leuven University Center for Computational Linguistics 
Maria-Theresiastraat 21 3000 Leuven, Belgium 
geert@ et.kuleuven.ac .be 
0. Abstract 
In this paper, we report on a large-scale conversion 
experiment with on-line valency dictionaries. A 
linguistically motivated valency dictionary in Prolog is 
converted into a valency dictionary for a large-scale 
machine translation system. Several aspects of the two 
dictionaries and their backgroand projects are discussed, 
as well as the way their representations are mapped. "/'he 
results of the conversion are looked at from an economic 
perspective (fast coding for NLP), and also from a 
computational-lexicographic perspective (requirements 
for conversions and for standardization of lexicon 
information). 
1. Introduction 
One of the major bottlenecks for large-scale NLP 
applications such as the METAL® MT system 1 is the 
acquisition of their lexicons 2. Whereas the development 
and fine-tuning of the grammars of such systems reaches 
its saturation point after a few years of R&D, the 
extension of their lexicons is a constant and ever 
growing concern. In order to speed up the lexical 
acquisition process, coding tools are developed to 
increase the human lexicographer's productivity and 
existing electronic dictionaries are looked for that can be 
converted and integrated with the particular NLP 
application at hand. 
In this paper we report on a large-seale conversion effort 
with an eye to enhancing the METAL verb dictionaries 
with several thousands of entries. While the system is 
capable of defaulting the necessary morphological 
information for verbs on the basis of their surface 
appearance (cp. Adriaens & Lemmens 1990), it cannot 
automatically create the complex syntactic-semantic 
valency information, i.e. the quantitative and qualitative 
characterization of the arguments of a verb. Still, this 
information is of crucial importance for the system to 
parse and translate correctly. Valency characterizations 
can be used to discriminate different readings of a 
sentence during analysis (cp. e.g. the different usages of 
hail: it is hailing, she hailed curses at me, he hailed me 
from the window, the people hailed him king). 
Moreover, they are often useful for disambiguating 
purposes with an eye to translation: for Dutch, for 
1 Metal® is a Siemens-Nixdorf (SNI) product. The 
University of Leuven co-develops the Dutch-French, 
French-Dutch and French-English language pairs with SNI. 
2 Cp. Walker, Zampolli & Calzolari forthcoming, 
Boguraev & Briscoe 1989, Zemik 1989. 
instance, to reach for something is a usage that needs a 
different translation from m reach somebody something 
(pakken versus overhandigen). (For a detailed discussion 
of the importance of valency for NLP and MT in 
particular, we refer to Gebraers 1991.) To recognize the 
need for detailed valency descriptions in NLP 
applications is one thing, to acquire them is less self- 
evident. In a system like METAL, the valency feature on 
verbs represents the most complex and hard-to-code 
element in its lexical representations. Hence, to 
automate and speed up the acquisition process, we used 
electronic valency dictionaries for Dutch and French as 
coded by the PROTON project (see van den Eynde et at. 
1988, Eggermont & van den Eynde 1990, Eggermont et 
at. forthcoming) as our starting point. The conversion 
was a non-trivial exercise in computational lexicography 
for several reasons. First, the PROTON databases are 
mainly descriptive and exhaustive in nature; they were 
not conceived with particular NLP applications in mind. 
METAL, on the other hand, seeks parsimony for 
efficient computational treatment within a machine 
translation application. More in particular, PROTON 
codes one entry per valency frame of a verb, whereas 
METAL merges valency patterns into "superframes", 
storing these only once for each verb. Second, their 
representation formalism is based on a particular 
distributional linguistic approach (the Pronominal 
Approach, see 2.2) not completely alien to the METAL 
representation, but not straightforwardly convertible 
either. And third, the PROTON databases take the form 
of Prolog clauses, whereas METAL uses Lisp lists. 
Beside the purely practical goal of fast lexicon extension, 
there are a few interesting questions to be asked that may 
be relevant beyond that goal: 
- Is such a conversion worth the eflort of defining a 
"waterproof" mapping between the source and target 
formalisms, and of developing the programs to do the 
mapping? In other words, could we trot simply have 
coded the several thousand verbs by hand instead of 
spending months on the conversion? 
- To what extent are these conversion experiments useful 
for an attempt at defining a theory-neutral standard for 
the representation of valency information in verb 
dictionaries for NLP applications? Or, less 
ambitiously, can we come up with a set of 
requirements for convertibility of lexieal resources? 
2. Verbal valency descriptions 
2.1 General considerations 
ACRES DE COL1NG-92, NANTES, 23-28 AOlrr 1992 1 l 8 2 PROC. OF COLING-92. NANTtT, S, AUG. 23-28, 1992 
lu linguistic ternls, veibal valeucy can be characterized as 
lexically controlled structural potential of a verb; in 
artificial intelligence terms, one would say that file verb 
has a frame structure with different role slols to be. filled 
by constituents in the sentcnce. Since the verb is often 
the nucleus of infornmtion arom|d which the different 
sentential elemeuts are orgtulized, it is inLportant for an 
NLP system to contain this valency info|mation. What 
then are file aspects of representation cue has to take lute 
account, ill llarticular with an eye to NLP applications? 
The first problem to be solved is what falls within file 
scope of the verb's valeucy (i.e. Ihe number and kind of 
valency-bound elements) and what falls outside of it (i.e. 
the free atljuncts of the sentcnce). An answer to this 
question leads to a quantitative classification (if verbs as 
nmnovalent (only one wllency element), bivalent (two) 
etc, and a qualitative classification of verbs as 
intransitive (subject, no ot~ject), Ir~msitive (subject and 
object), etc. Next, one bites tile problem of the 
distinction I)etweeu obligatoly and optiomd valency- 
bound elements (a distinction that is of particular 
importance to a role assignment algorithm). And finally, 
one must name, categorize and sulx;ategorize these 
elements, defining legal fillers lot a certain slot. If a verb 
has several valencies (corresponding to different 
syntactic/semantic readings), an additiunal 
representational matter to be handled (at a higher level of 
lexicou organization) is the way 1o store the different 
valencies. Are patterns stored ~parately with a repetition 
of the verb for each pattern? Can patterns be collap~d 
and stored just once with the verb? Decisions on these 
matters influence the database organization and 
consultation for NLP applications. In the next two 
subsections, we will show how the two formalisms 
between which the conversion was made try to provide 
answers to tile relu'e~ntation questions folmulated here, 
ill particular lot large ~ts of French and Dutch verbs. 
2.2 PROTON 
2.2.1 The PROTON project 
The Proton (Prolog en taalonderzoek, Prolog and 
linguistic research) project started in 1986 with as cue of 
its major ohjectives die COllStrnction of on-line valency 
dictionaries tor \[;reach and Dutch. The starting poiut was 
not a particular NLP application, but rather a linguistic 
concern for de~riptive correctness and completmless. 
Still, computational concerns were I)resent right froni 
the start, which led to the choicc of l'rolog as the 
declarative language for storing and processing the verbs 
(with processing ranging from sinlple retrieval of 
specific subsets of verbs to NLP applications in 
computer-aidcd language learning and parsing). Paper 
dictionaries, Ix)th gener',d (Le Petit Robert for French, 
Van Dale Basiswoordenboek tot Dutch) and valency 
dictionaries (Bus~ & Dubost 1983 for French) were used 
as background material. For the actual coding of the 
verbs, a particular distributional framework Ibrmed the 
basis, viz. the Pronominal Approach 3. Although there 
are many interesting sides to this approach (e.g. the 
exact methodology followed to determine reading 
3 See Blanche-Benveniste et al 1984 or Eggeimont et al 
1990 for full accounts of the Ih-onomiual Apltroach. 
distinctinns in verbs), we a~e mainly interested here in 
tile actual output of the lexicographic work, both 
quantitatively and for representatinn issues. A.s far as 
nunlbers are concerned, the cmrent status of the valency 
dictionaries of Dutch and French is the following. The 
Dutch valency dictionary contains about 4500 verbs; 
since each syntaelic/~mautic reading is coded separately, 
there are actually about 6300 valency llatterns coded. For 
French the two figures are 4(X)0 and 85004. (Note, in the 
passing that the frame/verb ratio is 1.3 fi~r Dutch and 2.1 
for French.) A rough estimate of the effort spent in 
doing this codiug is 2 man-years Ior French, 1 man-year" 
for Dutch. Tile diflerence is mainly due to the fact that 
French was file first lauguage Proton started out with; by 
the time Dutch was handled, coding experience mid 
c(xling Iools were available. 
2.2.2 The I'ROTON valency representation 
Proton database entries arc l'rolog facts, consisting of a 
three-place v ln'edicate; the three arguments are an 
identification mlml~cr, the verb's iufinitive, and a list 
structure containing the informatinn related to one 
valency realization. Due to space limitations, we have 1o 
~cfer to De Braekeleer 1991 tbr a fornlal account of this 
list structure; tor exmnples, we relier file reader tn SKX~tiOll 
4, For clarity's sake, we informally give the meaning of 
imlx)rtaut abbreviategl nt~tions: pO relates to the notion 
of subject, pl Io that el direct object, p2 to indirect 
object, p3 to a slx~cific prepositional object with de 
(related to French en), pprep to other prepositional 
objects, ploc/pmanner/ptemp/pqt 1o adverbial of 
Iocation/manner/time/quantitiy respectively. 
hi genelal, it cua be said that Proton valency entries arc 
dense in inl{trmatioo, hut (in the other hand souiewhat 
loosely structured. We will see below that NLP 
applicatkuls like Metal have a more rigid structure that 
is not so dense in information. For a conversion 
experiment this difference is both an advantage mid a 
disadvantage: the advantage is that one can go from 
structures that conlain more than one needs; rite 
di~ldvantage is that the determination of what maps to 
what is not straightfi~rwzu'd. 
2~3 METAL 
2.3.1. The METAl, system 
In contrast to Proton, Metal is a specific Nl.l' 
application, viz. a machine translation system, Its 
German-English, English-German, German-Spanisll, 
Dutch-French and French-Dutch systems arc 
conlmercially available; French-English, German- 
Danish, English-Spanish, Spanish-English and Russian~ 
German are under development. Full descriptions of the 
system can be found elsewhere 5. A brief account of 
4 In the course of 1991 the Frencll valency database will be 
ctJmnrercially available in electronic h~r~t (Eggermont et al. 
forthcoming). 
5 See Bennett & Slocum 1988, Thtumair 1989, Adriaens & 
Caeye~s 199(I fc~r general overviews; a general des~Tiption 
of the lexicon tbrmat call Ire found in Adriaens & l-emmens 
1990. 
Ac-rlis DE COLING-92, N^Nr~s, 23 28 AO(Yf 1992 l 1 g 3 Pace. OF COLING-92, NANrI!S, AUG. 23-28, 1992 
valency in Metal can be found in Gebrners 1988; an in- 
depth study of valency and machine translation bringing 
together work in the Proton and Metal projects is the 
topic of Gebrners 1991. Here, we will just give a general 
idea of the place of valency information in the Metal 
system and of how this information is used in the 
translation process. Valency patterns are stored as a 
feature-value pair on verbs in the monolingual 
dictionaries, in such a way that all patterns are coded 
only once with the verb; reading distinctions can give 
rise to different valency patterns, but even then they are 
all stored together with the verb. During analysis by an 
augmented context-free grammar (handled by a chart 
parser), rules at sentence level call a procedure for role 
assignment to the constituents of the sentence. This 
process is an intricate combination of general pattern 
matching algorithms and linguistically defined 
procedures (triggered by the valency information) for 
determining the best fitting valency pattern. In fact, the 
role assignment process can be said to consist of a 
grammar within the grammar, and a parser within the 
parser; it takes up a substantial proportion of the total 
time spent on sentence analysis. During transfer, valency 
information is again used (in the transfer dictionary) to 
disambiguate among different verb readings. For 
mapping into the target language, there are two 
approaches within Metal that have implications for the 
amount of valency-related information in the transfer 
dictionary. One approach tries to build a minimal 
hypothetical target language frame on the basis of the 
source role assignments and some crucial mapping 
information (e.g. for to like -> plaire, the subject is 
mapped into an indirect object, and the direct object 
becomes the subject: 1 like you -> Tu me plais). It then 
searches the monolingual target dictionary for a valency 
pattern that fits best with its hypothesis. The other 
approach tries to build the target frame without using the 
target dictionary at all: on the basis of the source role 
assignments and mapping information in the transfer 
dictionary, it builds the valency information for the 
target (see Gebruers 1991 for a detailed comparison of 
these approaches, with their advantages and 
disadvantages). In short, valency plays an important role 
in all phases of the translation process 6, involving 
complicated grammar and coding work. We conclude 
this brief sketch of valency in Metal by adding some 
figures of the size of the monolingual dictionaries. At 
the time of the conversion (March 1990), Metal 
contained 1600 Dutch verbs with 2050 valency patterns 
(a frame/verb ratio of 1.3) and 1055 French verbs with 
1600 patterns (a frame/verb ratio of 1.5). Let us add right 
away that partly thanks to the conversion effort we were 
able to increase these figures drastically in a short period 
of time (see section 4). Currently, there are 3000 Dutch 
verbs with 3700 valency patterns (frame/verb ratio = 1.2) 
and 2130 French verbs with 2850 valency patterns 
(frame/verb ratio = 1.3). In general, all other 
monolingual dictionaries of the commercially available 
systems (i.e. English, Spanish and German) also contain 
over 2000 verbs (2500, 2300 and 4000 respectively). 
6 See Gebmers 1991, 206-221 for an overview of valency 
treatment in other MT systems (TAUM, SUSY, GETA- 
ARIANE, VAPRE, EUROTRA). 
2.3.2 The METAL valency representation 
In METAL, valency is coded as one of the featare-value 
pairs on the lexicon entries for verbs (along with other 
information about morphology, syntax and semantics). 
Since the system is written in Lisp, its elements show 
the typical Lisp list structure. As for Proton, we have to 
refer to De Braekeleer 1991 for a full formal account of 
the METAL valency format; examples can be found in 
section 4. The meaning of some important abbreviations 
is the following: $SUBJ stands for subject, SDOBJ for 
direct object, $10BJ for indirect object, $ADV for 
adverbial complement, $POBJ for prepositional object, 
$SCOMP for subject complement, and $OCOMP for 
object complement. N1, NO, IMPS and ADJ indicate 
nominal, sentential, impersonal and adjectival 
subeategorizations respectively. Adverbial complements 
are further divided into LOC(ative), MAN(ner), 
MOV(ement), R(a)NG(e), T(e)MP(oral) and MEA(sure). 
Further subeategorization information is rendered as 
feature-value pairs, e.g. (TYPE P1) roughly corresponds 
to +human role fillers. Metal further uses the "OPT" 
atom in its valency patterns to indicate the optional 
valency-bound elements. Obligatory elements come first, 
those following the "OPT" atom are optional. Finally, 
the valency pattern contains General Frame Tests (after 
the "GFT' atom). These tests am executed before the role 
assigning mechanism tries to find fillers; they concern 
features that if present at the clause level should have 
specific values: the auxiliary (values are H/Z, 
hebben/zijn for Dutch; AlE avoirMtre for French) and the 
sentence's voice (VC; A/P, active~passive). It is 
interesting to note how in an application like Metal this 
kind of information (also present in the Proton 
descriptions) receives a special status with an eye to an 
efficient role assignment algorithm: if a valency pattern 
can be found not to apply because some restriction at the 
clause level is not satisfied, the pattern is discarded and 
no computation is wasted on checking the potential role 
fillers. 
3. Mapping PROTON to METAL 
it was already noted in 2.2.2 that the different origin of 
the two formalisms accounts for certain differences 
between them. Proton codes in an application-neutral 
fashion, exhaustively (aiming at descriptive adequacy), 
on a one-entry one-pattern basis, and in a relatively free 
format. Metal codes with an eye to a specific application 
(MT), pragmatically (what do we need for the application 
to run?), on a one-entry all-patterns basis (even 
collapsing some patterns in a superframe), and in a 
relatively rigid format easily digestible by software and 
lingware. Since the goal of the conversion was to derive 
the information needed in Metal, a f'wst step was to link 
all the Metal specifications to the corresponding Proton 
ones. Given the detailed nature of the Proton valency 
schemes, there were very few gaps in this mapping. One 
is worth mentioning, though. Proton does not go as far 
as Metal in the subcategorization of the adverbial 
complements (Metal's $ADVs); range and movement 
complements are not treated in a consistent way. Below, 
we show part of the resulting mapping table (not all 
subeategorization details are shown; see De Braekeleer 
1991, 61-62). It organizes the valency information from 
the Metal point of view: the relevant items are 
AC'I'ES DE COLING-92, NANTES, 23-28 AOt~'r 1992 l 1 8 4 PROC, OF COLING-92, NANTEs, AUO. 23-28, 1992 
optionality, naming of roles, categorization, 
subcategorization and general frame~sts. 
Proton Dutch Froton French 
OPT \[\[I ...\] \[\[l ...l 
$SUBJ t ~) !30 
$1X)BJ pl pl 
$1OBJ p2 p2('qui") 
SPOBJ pprep p2Cy"), p3, pprep 
$SCOMP (these two must be derived from 
$OCOMP several elements combined) 
$ADV advtype ~vtype 
cf. type 
cf. FCP / \[CP 
p(p0,\[' t'l) p(pO.\[qlT) 
related.~par adigms 
N1 
NO 
IMPS 
AI)J 
subcateeorizations 
ADXrI'YPE : LOC 
TMP 
MEA 
MAN 
RNG, MOV 
TYPE ; P1 
PO 
~eneral frametests: 
VC: A 
P 
AUX Dutch Z 
II 
French A 
E 
"wie" 
"war" 
ploc 
ptemp 
pqt 
tnnanner 
"qui" 
"que", "quoi" 
relatedpar, p(reform,\['pas si f fitre'l) 
related_par, absence of above 
p(mfonn,\['zijn+vd.', ...\]) 
p(refonn,\['perfectum hcbben', ...\]) 
auxiliary(\['avoir'l) 
auxiliary(\['b.tre'\] ) 
4. Aspects of the conversion software 
Ideally, the conversion should be a fully automatic 
process that t'alces the Proton database as input and 
delivers a Metal monolingual verb lexicon. Given that 
the Proton database also contains a field with several 
translations for each verb reading, we could even 
envisage creating transfer entries for the verbs as well. 
Yet, there are several reasons why we could only actfieve 
a semi-automatic conversion. As to the automatic 
generation of transfer entries, this idea had to be 
abandoned altogether, because it was too hard to pinpoint 
the distinctive information among the different patterns 
and translate that into contextual tests and actions in the 
Metal transfer dictionaries. Still, the translation field was 
preserved in the conversion output, so that 
lexicographers coding the transfer entries akeady lind ttte 
translations on-line. As to the fully automatic generation 
of a monolingual lexicon, several problems could not be 
overcome. First, we "already noted in the previous section 
that not all information needed for Metal was present in 
the Proton database; this implies that manual checks for 
completeness of the frames had to be made in any case. 
Second (and most important), we could find no 
satisfactory algorithmic solution to the problem of 
mapping rite one-entry one-valency-pattern organization 
of Proton into file one-entry all-patterns organization of 
Metal. Note that this is m)t a simple matter of collecting 
all the separately coded valency patterns for the same 
verb, anti storing them once as a long list with file verb 
ill the target database. For one thing, Metal does not 
ne.ed all possible valency patterns for its purpose of 
machine translation; the amomtt of patterns is kept as 
small as possible for efficient storage and computation 
rea~ns. Moreover, the patterns that remain are merged 
into "superpatterns" or "snperframes" as much as 
possible; where relevant for translation, the transfer 
dictionaries take them apart again. The way Mehd 
lexicographers decide on distingnislting valency patterns 
(verb readings) monolinguaUy proved hard to trmlslate 
into a foolproof algorithm; there are at tile most some 
intuitions, heuristics or rules of thumb. Hence, it was 
decided to convert on a per pattern basis, and leave the 
merging of patterns to rile human lexiGographer. 
The conversion software itself is written in Common 
Lisp (about 1000 lines of code). It works in two phases. 
First, the Protun Prolog clau~s pass through a finite- 
state transducer interpretiug them as plain character 
strings. The output of this pass is "lispified Prolog": 
Prolog chmses are turned into l.isp lists. At the same 
time, the necessary conversions at the character level arc 
taken care of: characters that would have a special 
meaning to the "Lisp reader" software (such as a comma 
or a backquole) are neutralized, and the extended ASCII- 
character sequences for aecentezl characters are turned into 
Metal's ISO-8859-1 characters. The ~cond pass parses 
the lists and converts them into structures whose most 
important field is the Metal frame. Additional software 
takes care of putting the Metal frames in their canonical 
order (i.e. a subject is coded before an object, etc.), and 
provides tools fi)r lexicographers to manipulate the 
conversion outpnt. As an illustration, we give one 
simple example of what the input and the outpot of the 
conversion look like: 
v (24720,'ddgager', 
\[ex('r : ddgager qqn d'une charge'), 
Ir(\['ontslaan (van)','ontheffen (van)'\]), 
p(pO,\[je,nous,on,qui,elle,il,ils,'cclui-ci','ceux -ci'\] ), 
p(pl.\[te,vous,'se r~.','l'un l'autre','se rdfl.', 
qui,la,le,les,'en Q','celui-ci','cenx- ci'\]), 
p(p3 ,\[en,'en~de inf)',quoi, 'celui-ci','ceux- 
ci','q a','~a(de h~ 0'\] ), 
p(reform,\['passif bare'\]), pivot(pl,pO,de inf,p3)l ). 
d~gager 
Example : (r : d~gager qqn d'une charge) 
Transfer : (outs|aan (van) omheffen (van)) 
Proton : ((reform passif bare) 
(p3 en en(de inf) quoi celui-ci eenx-ci cat qa(de inf)) 
(pl te veus se r&:. Fun l'aune se r6fl. qui la le 
les en Q celui-ci ceux-ci) 
(pOje uous on qui elte il ils celui-ci ceux~ci)) 
(($SUBJ N1 (TYPE PI)) 
($DOBJ NI (TYPE P1) (PRN RFX)) 
($POBJ NI (PREP de) NO (ICP de) (PIV 
$DOBJ))) 
5. Discussion of results 
Using the conversion software, the complete Proton 
database (at that time, i.e. March 1990, consisting of 
85130 valency structures for French and 600(I for Dutch) 
ACTES lYE COLING-92, NANTES, 23-28 AO~rr 1992 1 1 8 5 PRec. OF COL1NG-92, NANTES, AUG. 23-28, 1992 
was processed into a database with Metal valency 
patterns that could form the basis of manual coding. In 
the first place, checks were run to compare the results of 
the conversion with the frames already coded in the 
dictionary. This already led to an improvement of the 
existing database. In the second place, additional verb 
coding is now being done on the basis of the conversion 
output, and not from scratch (i.e. from paper 
dictionaries). 
The total effort spent on developing the software 
(including the preliminary study phas~ constructing the 
mapping table) was about four man-months. When we 
compared the time needed to code Metal valency frames 
starting from scratch (the way the first 1000 verbs were 
added to the system) with the time needed to code frames 
starting from the output of the conversion, we found that 
on the whole, and subtracting the conversion 
development effort, coding productivity is speeded up by 
a factor of 2. In other words, the practical goal of fast 
extension of the verb dictionaries was certainly reached. 
As to the more general questions of requirements for 
convertibility of lexical resources or standardization of 
lexical information, a few remarks are in place. First, in 
our case the input lexical resource was in a fairly easily 
convertible format, viz. Prolog clauses. Even so, since it 
was the fu'st time the Proton databases were used outside 
of the project, several ambiguities and inconsistencies 
were found that needed correction before the conversion 
could take place. A basic requirement for convertibility 
then seems to be a rigid description of the syntax and 
semantics of the database entries; before the resource is 
made available to the outside world, it should be checked 
thoroughly against its own specifications (parsers can be 
generated automatically on the basis of a BNF-like 
syntax). More ambitiously, if the formats of valency 
information in different applications were known, the 
resource could be made available along with converters 
or converter specifications. As to the long-term goal of 
standardization, we are planning to use the experiences 
gathered from the conversion (along with knowledge 
about other formalisms, like that used in EUROTRA or 
in the databases of the Nijmegen Centre for Lexical 
Information CELEX) to study the requirements for a 
theory-neutral and application-neutral standard for 
valency representation. Since valency is not restricted to 
verbs, but also concerns adjectives and nouns, the 
standard could even try to be category-neulxal as well. 
Although the Proton-Metal conversion proved a 
successful experiment in computational lexicography, 
many linguistic and computational issues concerning 
valency and its processing have not been touched upon 
here and certainly need further research. To name but a 
few: nominal and adjectival valency, a foolproof 
methodology for making and/or merging reading 
distinctions, valency and idiomatic expressions, the 
interactions of the different types of valency information 
in an NLP application, and the link with more 
semantically oriented approaches to valency. On the 
basis of the availability of large amounts of valency 
data, and the experience with different formalisms, we 
hope to be able to tackle some of these issues in the 
future. 
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