Multiword Lexical Acquisition and Dictionary Formalization 
Cristina MOTA 
LabEL, CAUTL, IST 
Av. Rovisco Pais 
Lisboa, Portugal, 1049-001 
cristina@label.ist.utl.pt 
Paula CARVALHO 
University of Lisbon and 
LabEL, CAUTL, IST  
Av. Rovisco Pais 
Lisboa, Portugal, 1049-001 
paula@label.ist.utl.pt 
Elisabete RANCHHOD 
University of Lisbon and 
LabEL, CAUTL, IST  
Av. Rovisco Pais 
Lisboa, Portugal, 1049-001 
elisabet@label.ist.utl.pt 
 
Abstract 
In this paper, we present the current state of 
development of a large-scale lexicon built at 
LabEL1 for Portuguese. We will concentrate on 
multiword expressions (MWE), particularly on 
multiword nouns, (i) illustrating their most relevant 
morphological features, and (ii) pointing out the 
methods and techniques adopted to generate  the 
inflected forms from lemmas. Moreover, we 
describe a corpus-based aproach for the acquisition 
of new multiword nouns, which  led to a 
significant enlargement of the existing lexicon. 
Evaluation results concerning lexical coverage in 
the corpus are also discussed. 
1 Introduction 
MWEs have been viewed, for long time, as 
marginal idiosyncratic combinations of words. In 
recent years, however, there has been a growing 
awareness in the NLP community of the problems 
that MWEs pose and the need for their robust 
handling. Several major conferences and satellite 
workshops have been dedicated to the subject 
(ACL, EACL, LREC, for instance); major 
publications devote thematic issues to MWEs. 
Anticipating that growing interest, over the last 
years, a significant part of LabEL’s research has 
been devoted to the development of large-scale, 
linguistically precise language resources, namely 
to the construction of computational lexicons for 
simple  and multiword units (Eleutério et al., 1995; 
Ranchhod et al., 1999; Ranchhod et al., 2004). 
In fact, we have observed that MWEs are used 
frequently in both everyday language and technical 
and scientific texts to express ideas and concepts 
that in general cannot be stated by “free” linguistic 
structures. They include a large range of different 
linguistic phenomena, such as: (i) lexical 
compounds (nouns: cellular phone, rush hour, New 
Jersey; adjectives: well-known; adverbs: for the 
time being, in short; prepositions and conjunctions: 
                                                     
1 LabEL (Laboratório de Engenharia da Linguagem) 
http://label.ist.utl.pt 
in spite of, in order to) (ii) phrasal verbs (give up); 
(iii) light verbs (give a lecture); (iv) fixed 
(proverbs and maxims) and semi-fixed sentences 
(to see the light at the end of the tunnel; to take the 
Lord’ name in vain). From a linguistic point of 
view, all these expressions exhibit distributional 
and selectional constraints, i.e. they lack 
compositionality, and frequently have idiomatic 
interpretations. 
In this paper, we focus on multiword nouns. 
Special attention will be given to their 
formalization and generation, using INTEX, a 
public FST (Finite-State Transducer) based NLP 
system [Silberztein, 1993]. In this context, we 
present the main characteristics of a new 
inflectional module, conceived at LabEL, fully 
compatible with this system. Next, we describe the 
acquisition methodology used to gather new 
dictionary entries in a fragment (extracts 1,520,001 
to 1,567,625) of the non-annotated version of a 
public Portuguese corpus, CETEMPublico2. 
Finally, based on this experiment, we assess, on 
the one hand, the  dictionary increase, and, on the 
other hand, the lexical coverage improvement in 
the referred corpus. 
2 Characterization of Multiword Nouns 
Multiword (or compound) nouns are composed of 
non-capitalized simple words. Superficially, they 
seem to result from general rules of word 
combinations but they present constraints 
(morphological, combinatorial, etc.) concerning 
the properties they were supposed to have. 
Regarding inflection, general rules presented by 
grammarians do apply to some cases, but most 
compounds exhibit inflectional restrictions on 
gender or number that cannot be described by the 
morphological properties of their constituents. 
Table 1 presents a few examples of the most 
representative classes of compound nouns in 
Portuguese. 
                                                     
2 CETEMPublico is a journalistic corpus containing 
about 180 million words (see Santos and Rocha, 2001 
for techical information). 
 
  	 
 	        

 

 
  
 

 
 	 
  
 	 
  	 
 
  	 
 
 	 
     	 
 
 	 
       
    	 
 
 	 
  	 
 
 	 
   
  	 
 
                  
  
        	     	 
     
  	      
   
       
  
           
     
   	 
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Table 1: Some binary compound noun classes  
These classes represent binary compounds, 
comprised of two content words (where one of 
them is a noun), eventually connected by a 
grammatical word3. 
The classification criteria are based on the  
noun’s internal structures, which are generally 
associated with a characteristic inflectional pattern. 
For instance, compound nouns belonging to the 
NA class usually allow the inflection in gender 
and/or number of both constituents (e.g. bomba 
atómica, bombas atómicas); on the contrary, in the 
majority of NDN compound nouns, only the first 
noun can inflect (conselho de guerra, conselhos de 
guerra). 
In the following sections, further relevant 
information on inflection, formalization and 
generation of inflected forms will be given. 
3 Formalization of NA and NDN Nouns 
Following methods and formalisms introduced at 
LADL [Gross, 1988; Courtois and  Silberztein, 
1990], linguistic attributes of simple and 
multiword units are systematically encoded in 
dictionaries compatible with INTEX.   
In this system, compound word entries are 
handled depending on their internal structure. They 
are formalized and processed separately by 
different programs.  
In order to simplify the formalization of 
linguistic attributes, and make the generation 
process easier, we implemented a new inflectional 
module compatible with INTEX system. The main 
strength of this tool is allowing the simultaneous 
generation of all compounds, regardless of their 
internal structure, reusing the inflectional graphs 
already built for simple words [Mota, 
forthcoming]. The morphological constraints are 
specified manually, assigning to each constituent 
the inflectional code that corresponds to its 
inflectional behavior within the compound, as 
illustrated by the following dictionary entries: 
actor(N040) secundário(A001),N+NA+Hum 
                                                     
3 Even though less productive than the previous 
structures, there are longer multi-word combinations 
that may involve more than one compound form (e.g. 
cabo de alta tensão, high-tension electricity cable). 
ser(N205) humano(A201),N+NA+Hum 
ponto(N201) de vista,N+NDN+Pred 
direitos(N292) de autor,N+NDN 
In the first compound, both constituents keep 
their simple word dictionary inflectional code; they 
inflect in gender and number, according to the 
compound inflectional behavior.  
On the other hand, the compound noun ser 
humano inflects only in number, which means that 
the masculine nominal constituent ser preserves its 
inflectional code, but the adjectival constituent 
humano (which also inflects in gender, as simple 
word) receives a new code that just allows its 
inflection in number within the compound. 
In the case of the NDN compounds, as 
previously mentioned, only the head can inflect: 
ponto de vista inflects in number, so the head 
receives a code allowing its inflection; direitos de 
autor does not inflect (it is an exclusive masculine 
plural noun), hence the head is assigned an 
inflectional code that simply transmits these gender 
and number features to the compound.   
4 Generation of Inflected Forms 
The following example illustrates how the new 
inflection module uses the dictionary information, 
briefly presented in the previous section: 
actor(N040) secundário(A001),N+NA 
Inititally, the inflectional module generates the 
inflected forms of the noun actor, based on the 
inflectional paradigm described in the graph N040. 
Then, it combines the resulting inflected nouns 
with all inflected forms of the adjective 
secundário, generated given graph A001. 
Subsequentely, the combinations that do not verify 
the gender and number agreement constraints are 
eliminated. Additionally, the constituent 
inflectional attributes are inherited by the 
compound.  
As a result, the following entries are obtained: 
actor secundário,actor secundário.N+NA:ms 
actriz secundária,actor secundário.N+NA:fs 
actores secundários,actor secundário.N+NA:mp 
actrizes secundárias,actor secundário.N+NA:fp 
This example illustrates the case where both 
words that constitute the compound have similar 
inflectional features. These attributes are directly 
transferred to the inflected forms of the compound. 
When one of the compound constituents does 
not have either gender or number explicit 
morphemes, as artista (which can be either a 
masculine or a feminine singular form) in the 
following entry: 
artista(N101) plástico(A001),N+NA 
 
the inflectional module assigns to the compound 
the morphological attributes of the constituent that 
has explicit gender and/or number morphems (in 
this case, plástico). 
The compounds just illustrated belong to the NA 
class4. The inflection of NDN forms simply 
corresponds to the inflection of the head noun and 
assigment of its attributes to the compoud.     
5 Acquisition of New Entries 
With the purpose of increasing the number of the 
most representative nominal entries (NA and 
NDN)  in the common multiword dictionaries, we 
used a corpus-based approach. 
In the first stage, candidates were automatically 
extracted from a fragment of CETEMPublico 
corpus (from now on, acquisition corpus), using 
INTEX. After tokenized by INTEX, 6,385,531 
tokens (corresponding to 138,230 different tokens) 
were identified in the acquisition corpus. From 
those, 5,162,111 (138,174 different forms) are 
alphabetic words.  
LabEL’s simple and multiword electronic 
dictionaries were then applied to the acquisition 
corpus. These dictionaries contained 171,159 
nominal entries, from which 82% were simple 
words and 18% compounds. From the 22,581 
compounds, 61,7% are NA, 33,6% are NDN and 
the remaining 4,7% belong to other strucutres.  
Candidate identification was performed using 
the elementary regular expressions <N>de<N> 
and (<N:ms><A:ms> + <N:fs><A:fs> + 
<N:mp><A:mp> +  <N:fp><A:fp>)5. They match 
nominal compounds presenting, respectively, an 
NDN and an NA structure. With respect to the 
latter structure, the expression also guarantees 
morphological agreement between nouns and 
adjectives. 
Such expressions recognized in the acquisition 
corpus 242,527 (187,146 different forms) 
candidates, from which 117,616 (69,066 different 
forms) are NDN structures and 230,761 (118,080 
different forms) are NA structures. 
Each class’s candidates were integrated into a 
concordance, to which were applied the existing 
compound dictionaries. The resulting list of non-
recognized candidates was then manually reviewed 
by linguists, aiming the selection and linguistic 
formalization of valid compounds. Graphic 1 
reflects the effort involved in the selection 
procedure. 
                                                     
4 This procedure also applies to other compounds 
composed of two or more elements that inflect (e.g. 
acidente(N201) vascular(A205) cerebral(A211), stroke). 
5 Regular expressions are written according to the 
INTEX format. 
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Locate NA Locate NDN
 
Graphic 1: NA and NDN candidates  
One clear observation is that there is a great 
discrepancy between the initial candidate lists 
(NA: 238,313; NDN: 116,246) and the final 
selected compound forms (NA: 21,289; NDN: 
3750)6. The percentage in the graphic was 
calculated based on the number of non-recognized 
different candidates (NA: 104,715; NDN: 56,741). 
Another interesting observation regarding 
Graphic 1 is that the size of the NA candidate list 
is slightly more than a double of the NDN 
candidate list. Nevertheless, the NA candidate list 
includes proportionally more valid compound 
forms (NA: 20%; NDN: 7%). In addition, the final 
selected NA compound list contains about five 
times more entries than the corresponding NDN 
list.   
The selected compound forms resulted in a total 
of 19,825 NA and 3,769 NDN canonical entries, 
which correspond respectively to 41,267 NA and 
7,722 NDN inflected forms. 
6 Evaluation 
Before lexical acquisition, the application of 
LabEL’s lexical resources to the acquistion corpus 
allowed assigning 94,229 different simple word 
tags (34,892 nouns) and 15,120 different 
compound tags (13,594 nouns). Hence, the 
compound noun percentage is low (28%), 
compared to the number of different nominal tags 
assigned to simple forms. This discrepancy is not 
unexpected, regarding the low number of nominal 
compound entries in dictionaries. 
As previously mentioned, the gathering of NA 
and NDN compounds in the acquisition corpus led 
to the formalization of 23,594 canonical entries. 
Accordingly, the inflected form dictionary 
increased approximately 3 to 4 times (more 53,815 
entries, in a total of 76,396 entries). 
                                                     
6 In this study, we did not assess the list of candidates 
recognized by dictionaries, which means that we did not 
count the hypothetical cases of embedded compound 
forms (e.g. cabo de alta-tensão, high-tension electricity 
cable). 
 
When we apply the enlarged dictionary to the 
same corpus, we observe that the percentage of 
compound nouns with respect to the total of 
nominal tags incresead  significantly. Now, 40,902 
different compound forms were identified, which 
means that more than a half of the nouns in the 
corpus correspond to multiwords.  
Considering the compound occurrences in the 
acquisition corpus, Graphic 2 illustrates their 
frequency distribution. 
0%5% 6%
3%
4%
8%
16%
58%
Occ >100
Occ 50-100
Occ 10-50
Occ 5-10
Occ 5
Occ 4
Occ 3
Occ 2
Occ 1
 
Graphic 2: Compound noun frequency  
It is important to draw attention to the fact that 
89% of compound forms occur less than five 
times; in particular, 58% occur just once. 
These figures demonstrate that, contrary to what 
is observed with simple nouns, which are very 
recurrent in texts, the average number of 
compound occurrences is, in general, extremely 
low. This evidence raises the question whether 
statistical methods, based on frequencies, can 
adequately handle the majority of compound 
forms. 
Regardless of wether the compound acquisition 
has been done exclusively in a fragment of 
CETEMPúblico, the application of the new 
dictionary to the remaining fragments of this 
corpus also increased the number of tags assigned 
to compound words. 
On average, before lexical acquisition about 
13,000 different compound nouns were 
recognized; this number more than doubled 
(approx. 33,000) when we applied the enlarged 
dictionaries to the other fragments. As mentioned 
before, in the acquisition corpus, the number of 
compound tags exceeded the number of simple 
noun tags. So, we may infer that a similar behavior 
would be expected in the remaining fragments, if 
they were also considered. 
7 Final Remarks 
In this paper, we described a new FST-based 
compound inflectional tool. The main advantage of 
this inflectional module is reusing INTEX simple 
word inflectional graphs in the simultaneous  
generation of all compound, regardless of their 
internal structure. Even though it has only been 
tested in the formalization and generation of 
Portuguese compound nouns, we believe it can be 
easily adapted to handle other languages having 
similar compound inflectional behavior.  
A corpus-based approach to multiword 
acquisition was also presented. We showed that, in 
spite of involving human effort, the results 
obtained effectively improved dictionary coverage. 
Moreover, the results concerning compound noun 
frequency raised the question whether statistical 
approaches, based on word frequencies, are 
(un)adequate to extract multiword nouns from 
texts. 
8 Aknowlegments 
This work was partially supported by FCT 
(POSI/39806/PLP/2001; POSI/39806/PLP/2001). 

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