Automatic Processing 
of Proper Names in Texts 
Francis Wolinski I 2 Frantz Vichot I Bruno Dillet 1 
1 Informatique CDC 2 LAFORIA 
Caisse des D@6ts et Consignations Universit~ de Paris VI 
France France 
E-mail: { wolinski,vichot,dillet } @icdc.fr 
Abstract 
This paper shows first the problems raised by proper 
names in natural language processing. Second, it in- 
troduces the knowledge representation structure we 
use based on conceptual graphs. Then it explains 
the techniques which are used to process known and 
unknown proper names. At last, it gives the perfor- 
mance of the system and the further works we intend 
to deal with. 
or unknown. Some of these techniques are taken 
out of existing systems but they have been uni- 
fied and completed in constructing this single oper- 
ational module. Besides some innovative techniques 
for desambiguating known proper names using the 
context have been implemented. 
2 Problems raised by proper 
names in NLP 
1 Introduction 
The Exoseme system \[6, 7\] is an operational applica- 
tion which continuously analyses the economic flow 
from Agence France Presse (AFP). AFP, which cov- 
ers the current economic life of the major industri- 
alised countries, transmits on average 400 dispatches 
per day on this flow. Their content is drafted in 
French in a journalistic style. Using this flow, Ex- 
oseme feeds various users concerning precise and 
varied subjects, for example, rating announcements, 
company results, acquisitions, sectors of activity, ob- 
servation of competition, partners or clients, etc. 50 
such themes have currently been developed. They 
rely on precise filtering of dispatches with highlight- 
ing of sentences for fast reading. 
Exoseme is composed of several modules : a mor- 
phological analyser, a proper name module, a syn- 
tactical analyser, a semantic analyser and a filtering 
module. The proper name module has two goals : 
segmenting and categorising proper names. During 
the whole processing of a dispatch, the proper name 
module is involved in three different steps. First, 
it segments proper names during the morphological 
analysis. Second, it categorises proper names dur- 
ing the semantic analysis. Third, it is invoked by 
the filtering module to supply some more informa- 
tion needed for routing the dispatch. 
The proper name module is based on different 
techniques which are used to detect and categorise 
proper names depending on whether they are known 
In the AFP flow, proper names constitute a signif- 
icant part of the text. They account for approxi- 
mately one third of noun groups and half the words 
used in proper names do not belong to the French 
vocabulary (e.g. family names, names of locations, 
foreign words). In addition, the number of words 
used in constructing proper names is potentially in- 
finite. 
The first step of the processing is segmentation, 
i.e. accurate cutting-up of proper names in the text; 
the second step is categorisation, i.e. the attribution 
to each proper name of a conceptual category (indi- 
vidual, company, location, etc.). It should be noted 
that segmentation and categorisation are processed 
differently depending on whether the proper name 
is known or unknown. 
2.1 Segmentation of proper names 
The segmentation of proper names enables the 
synctactical analyser to be relieved, particularly 
in the case of long proper names which contain 
grammatical markers (e.g. prepositions, conjunc- 
tions, commas, full stops). As illustrated in \[4\], 
segmentation firstly prevents long proper names 
from undertaking pointless analyses. For exam- 
ple, for Caisse de ¢r6dit hgricole du Morbihan the 
analyser will provide two interpretations depend- 
ing on whether Morbiha.n is attached to Cr6dit 
hgricole or to Caisse. Moreover, proper names of- 
ten constitute agrammatical segments that some- 
times confuse the synctactical analyser. For exam- 
23 
ple, in the sentence The director of Dollfus, Mieg 
and Cie has announced positive results, the anal- 
yser has difficulties in finding that The director is 
the subject of announce if it does not know the 
company Dollfus, Mieg and Cie. In the Exoseme 
process, the Sylex syntactical analyser \[3\] delegates 
the segmentation of these agrammatical gaps to our 
proper name module. 
Segmentation of known proper names has al- 
ready been studied and is treated in some systems 
such as NameFinder \[5\]; segmentation of unknown 
proper na,nes based on pattern matching is imple- 
mented in several systems \[1, 2, 4, 9\]; the morpho- 
logical matching of acronyms is described in \[11\]. 
2.2 Categorisation of proper names 
Once the segmentation has been achieved, categori- 
sation of proper names is necessary for the seman- 
tic analyse> Categorisation maps proper names 
into a set of concepts (e.g. human being, company, 
location). The very nature of proper names con- 
tributes widely to the understandin.g of texts. The 
semantic analyser must be able to use the various 
categories of proper names as semantic constraints 
which are complementary for the understanding of 
texts. For example, in the filtering theme of acqui- 
sitions, the sentence Express group intends to sell 
Le Point for 700 MF indicates a sale of interests in 
the newspaper Le Point. Whereas the following sen- 
tence, which is grammatically identical to the pre- 
ceding one, Compagnie des Signaux intends to sell 
TVM430 for 700 MF indicates only a price for an 
industrial product. 
Categorisation of unknown proper names has al- 
ready been studied as well. Particularly, categori- 
sation of unknown proper names is automatically 
acquired in pattern matching techniques quoted in 
previous section; rules using the context of proper 
names in order to categorise them are also imple- 
mented in \[2, 9\]. 
In our system, these ontological categories are 
extended to attributes needed by the semantic anal- 
yser or the filtering module. For instance, proper 
names may have different attributes such as city, 
rating agencies, sector of activity, market, financial 
indexes, etc. 
3 Representation of proper 
names 
We will see that the proper name module requires 
a large amount of information concerning proper 
names, their forms, their categories, their attributes, 
the words of which they are composed, etc. This in- 
formation must be able to be enriched in order to 
include additional processes, and accessible in order 
to be shared by several processes. We use a repre- 
sentation system similar to conceptual graphs \[10\], 
the flexibility of which effectively gives expressive- 
ness, reusability and the possibility of further devel- 
opment. It enables indispensable and heterogeneous 
data to be memorised and used in order to process 
proper names. 
For a given proper name, its category and its var- 
ious attributes are directly represented in the form 
of a conceptual graph. For example, our knowledge 
base contains the graphs of Figure 1. This simple 
representation will be completed in the subsequent 
sections. We are going to show how each encoun- 
tered problem uses the information of tile knowledge 
base and may add its own information to it. 
The final result is a large knowledge base in- 
cluding 8,000 proper names sharing 10,000 fornas, 
based on 11,000 words. There are also 90 at- 
tributes of proper names or words. Each new filter- 
ing theme may be a special case and its implemen- 
tation may lead to introduce additional attributes 
into the knowledge base. The adopted representa- 
tional formalism enables these additions to be made 
without leading to substantial modifications of its 
structure. 
4 Processing known proper 
names 
Firstly, we recognise the proper names in which we 
are directly interested in order to allocate to them 
attributes which are required for subsequent pro- 
cesses. We also seek to recognise the most frequent 
proper names (e.g. country, cities, regions, states- 
men) in order to segment them and categorise them 
correctly. 
4.1 Immediate recognition 
The first idea which comes to mind is to memorise 
the proper names as they are encountered in the 
dispatches and to allocate to them the attributes. 
All this information is stored in the knowledge base 
which contains, for example : 
• ' 'New' ' + ' 'York' ' --* PN -~ location 
• '~Soci4t4'' + ~G4n4rale'' --+ PN--+ bank 
• '~Standard'' + ~and'~ + ''Poor's ~ ' --~ PN 
--+ rating agency 
The knowledge base is thus structured on the 
model showed in Figure 2. And subsequently, recog- 
nition of the proper name in the text occurs through 
simple pattern matching. 
24 
I PN 'Paris' I I PN 'City of Saint-Louis' I PN 'Group Saint-Louis' 1 
Figure 1: Representation of Proper Names 
I PN 'Eridiana Beghin Say' \] 
\[ oompa~y I I,oo~io~l 
Figure 2: Words composing Proper Names 
1 1 
"Boris" ~followed_by)-~-~l-"Eltsine" 
I PN 'Boris Eltsine' 1 
Figure 3: Equivalent Words 
25 
4.2 "Equivalent" words 
However, words lnaking up proper names accept. 
many slippages which result from abbreviat, ions, 
translation, common faults, etc. For example : 
• Standard and Poor's : 
Standard and Poors, Standard et Poor's 
• Soci~t~ G~n~rale : 
Soc. gen., St~ g~n~rale 
• Boris Eltsine : 
Boris Elstine, Boris Etlsine, Boris Yeltsine 
In order to avoid listing pointlessly all the forms 
that a proper name can take, through slippages of its 
words, certain variations in the recorded form are au- 
thorised. To this end, slippages in a given word are 
grouped around an "equivalent". This technique, 
which has been developed in the NameFinder sys- 
tem \[5\], under the term "alternative" words, enables 
to make a correspondence with different forms likely 
to appear. 
Equivalent words are expressed in the knowledge 
base through a relationship. For example, our base 
contains the graph of Figure 3. 
4.3 Synonymous proper names 
However, one can use very different proper names 
to designate a given reality. For example, we can 
find simple synonyms such as Hexagone for France 
or Rue d'Antin for Paribas. This notion is similar 
to alternative names in \[5\]. Dispatches also contain 
more or less complex transformations, that it can 
be difficult to derive from the standard form, such 
as NewYork and NY for New York, or indeed SetP and 
S-Poors for Standard and Poor's. 
Once again, in order to avoid listing pointlessly 
the attributes for all the necessary proper names, 
the forms of synonymous proper names are grouped 
around a single reference to which the various at- 
tributes are allocated. This grouping enables the 
various references memorised to be represented, and 
their attributes to be factorised. The knowledge 
base is modified according to the enriched model 
showed in Figure 4. 
4.4 Disambiguating proper names 
When a user is interested in a given proper name, it 
is not sufficient to look for it through the dispatches 
since a simple selection on this name frequently pro- 
duces homonyms. Such interference, which is annoy- 
ing for users, reflects the limitations of traditional 
keyword systems. In the AFP flow, for example, the 
form Saint-Louis may designate equally well: 
• the capital of Missouri, 
• a french group in the food production industry, 
• les Cristalleries de Saint Louis, 
• a small town in Bas-Rhin province, 
• an hospital in Paris, 
The crucial problem posed is to succeed in dis- 
ambiguating this type of forms. Or, in other words, 
in determining, or at least in delimiting, the denoted 
reference. 
4.4.1 Disambiguating through the local con- 
text 
Exploration of the local context using the proper 
name can in certain cases enable a choice to be made 
between these various references. If the text speaks 
of St-Louis (Missouri), only the first interpretation 
will be adopted, if the knowledge base contains the 
information that Saint-Louis is in the United States, 
and if a rule is able to interpret the affixing of a 
parenthesis. We are currently working on this del- 
icate aspect in order to unify all the rules we have 
accumulated for resolving concrete cases. We are 
aware that these types of inference are comparable 
to the micro-theories of the Cyc project \[8\] in which 
the need for a great amount of information is the 
main thesis. 
We will see in section 5.2.1 that the local con- 
text may categorise an unknown proper name and 
therefore it may help to desambiguate an ambigu- 
ous known proper name. For instance, if the text 
speaks of the mayor of St-Louis, the company and 
hospital can certainly be ruled out. 
4.4.2 Disambiguating through the global 
context 
Abbreviations of proper names are another, much 
more frequent, source of ambiguities. Depending 
on the context, la G6n6rale may designate Soci~t~ 
G4n4rale, Compagnie G4n~rale des Eaux or indeed 
G4n~rale de Sucri~re. Similarly, acronyms, which 
are almost always common to several proper names, 
constitute an extreme form of abbreviation. We thus 
discover from time to time new organisations which 
share the acronym CDC with Caisse des D~p6ts et 
Consignat ion. 
In general, ambiguous forms are not used on their 
own in dispatches, and other non-ambiguous forms 
appear. Their presence consequently enables the 
ambiguity to be removed. If the proper names Saint 
Louis and H6pital Saint Louis appear in a single 
dispatch, for example, the reference corresponding 
to the hospital will have more forms than each of 
the others and will thus be the only one adopted. 
26 
Consequently, when there is an interest in an 
individual reference and the corpus has revealed 
homonyms, we record them in the knowledge base. 
We link them with the individual reference in order 
to be able to manage the ambiguities. 
Nevertheless, when the ambiguity is unable to 
be removed, we choose the most frequent interpre- 
tation, but the user is told of the doubtful nature 
of our choice. In the dispatch title "Saint Louis: 
results up", for example, the proper name Saint 
Louis is processed as the food production group, 
which is the most frequent ease, although it could 
equally well designate les Cristalleries. 
5 Processing unknown proper 
names 
The preceding techniques tackled the problem of the 
variability of known proper names. However, al- 
though many proper names appear frequently, oth- 
ers appear only once. Even if the constituted knowl- 
edge base is very comprehensive, it is absolutely'im- 
possible to record all potential proper names. We 
have therefore to deal with unknown proper names. 
5.1 Prototypes of proper names 
As fully explained in \[2\], some proper names are con- 
structed according to prototypes which enable them 
to be categorised through their appearance alone. 
For example : 
• known-first-name + unknown-upcase-word --* 
human being (e.g. Andr4 Blavier) 
• unknown-upcase-word + company-legal-form 
--+ company (e.g. KyoceraCorp) 
unknown-upcase-word + ~'-sur-'' + 
unknown-upcase-word--+location 
(e.g. Cond&sur-Huisne) 
Furthermore, certain categories of proper names 
accept traditional extensions which it is also possible 
to detect. For example : 
• known-human-being + human-title --+ 
human being (e.g. Kennedy Jr) 
• known-company + company-activity--+ company 
(e.g. Honda Motor) 
known-company + ' '-' ' + known-location , --+ 
company (e.g. IBM-France) 
• known-human-being + company-activity -~ 
company (e.g. Bernard Tapie Finance) 
Lastly, such extensions may be combined, 
e.g, "Siam Nissan Automobile Co Ltd" is probably a 
subsidiary of Nissan. 
These prototypes enable bot\]~ to segment and 
categorise proper names. Of course, they do not 
constitute infallible rules (for example, Guy Laroche 
is a company while its prototype makes one believe 
it is a human being) but they give correct results in 
a large majority of cases. 
In order to use these prototypes, we build a 
rulebase for detecting and extending proper names. 
Moreover, we add some attributes to the existing 
words in our knowledge base (e.g. first names, legal 
company forms, company activities). For example, 
it contains the graph of Figure 5. 
5.2 Other techniques of categorisa- 
tion 
Nevertheless, a prototype is not always enough to 
categorise a proper name. In particular, an isolated 
proper name does not enable one to infer its category 
directly. For example, who can say simply on sight 
of the proper name that Peskine is an individual, 
Fibaly a company and Gisenyi a town ? 
5.2.1 Categorisation through the local con- 
text 
However, the text often contains elements enabling 
one to deduce the category of a proper name \[2\]. 
To this end, rules using the local context give good 
results. For example : 
,, apposition of an individual's position : 
Peskine, director of the group, 
* name complement typical of a company : 
the shareholders of Fibaly 
• name complement typical of a location : 
the mayor of Gisenyi. 
These rules once again require that certain words 
from the knowledge base are marked by individual 
attributes. For example, the word "mayor" has both 
the following attributes : 
• human-being-apposition : 
(e.g. Chirac, mayor of the town) 
• location-name-complement : 
(e.g. the mayor of Royan) 
27 
i "soc,ete" I--'-~-'-I"Geoera,e" I 
I"Socie'~eoe,a'o" I I "SocGen" I 
company 
Figure 4: Synonymous Proper Names 
--~ t,,Thomsoo,,1--~ 
I "IBM C 
'~ ~ ~~,Ref '~~romson'J 
Figure 5: Words and Proper Names Attributes 
28 
5.2.2 Categorisation through the global con- 
text 
However, the local context of a proper name does not 
necessarily enable one to infer its category. For in- 
stance, the mere radical of a proper name (e.g. fam- 
ily name, main company) is often used later in the 
text instead of the full name. The company Kyocera 
Corp, for example, may be designated by the single 
word Kyocera in the remainder of the text. 
Consequently, for each unknown proper name, 
we look to see whether it does not appear in another 
proper name in the text. In this case, we estab- 
lish a link between these two proper names in order 
to transfer the attributes of the recognised proper 
name to this new proper name. However, one should 
always beware since different proper names some- 
times share the same radical : Mr Mitterand and Mrs 
Mitterand, or again Mr Bollor4 and Bollor6 Group. 
Although, in the most frequent cases, we resolve this 
well-known problem but as in \[11\] we do not have a 
general solution. 
5.3 Matching acronyms 
Acronyms occur frequently in AFP dispaches. On 
one hand, the linguistical construction of the cor- 
responding text of acronyms may be relatively com- 
plex. On the other hand, in some case, the relatively 
simple morphological construction of acronyms may 
be treated with a simple pattern matching with 
the corresponding text. Moreover, acronyms are 
widespread ambiguous forms of which it is unthink- 
able to list all cases and we have seen in section 
4.4.2 that desambiguation of proper names needed 
to memorize all potential homonyms. Therefore, 
a process for dealing with acronyms will first seg- 
ment these unknown proper names and second de- 
sambiguate these potential homonylns. 
In general, when an acronym is introduced in a 
text, its complete form is given using parentheses. 
For example : 
• International Primary Aluminium Institute 
(IPAI) 
• AIEA (Agence Internationale de i' Energie 
Atomique) 
• Centre de recherche, d'~tudes et de 
documentation en 4conomie de la sant~ 
(CREDES) 
As observed in \[11\], it is possible to explore the 
local structure of the parentheses in order to de- 
termine whether the acronym corresponds to the 
complete form and, if so, the acronym and the full 
name are propagated throughout the remainder of 
the text. Some words (e.g. articles, prepositions) 
may be jumped when matching up acronyms and 
text. For example, the acronym SHF of Soci6t4 des 
Bourses Fran~aises omits the preposition "des", 
while the acronym BDF of Banque de France keeps the 
"de". In order for our processing module to recog- 
nise these words, we allocate a special attribute to 
them in the knowledge base. 
This simple and effective technique enables most 
of the acronyms introduced to be processed cor- 
rectly. Only foreign acronyms accompanied by their 
translation are not processed. 
6 Results and prospects 
Built for an operationnal system which filters in real 
time AFP dispatches, we have presented the mod- 
ule for the automatic processing of proper names. 
This module unifies and completes known techniques 
which enable to segment and categorise proper 
names. Particularly, we have explained our inno- 
vative technique for disambiguating known proper 
names and its relationship with the techniques for 
categorising unknown proper names and for match- 
ing acronyms. Our system currently detects 90% 
of proper names in AFP dispatches and categorises 
85% of them correctly. The full Exoseme pro- 
cess is undertaken in approximately 14 seconds 
per dispatch on a SUN SPARC 10, i.e. in 1,400 
words/minute approximately. 
We consider continuing with our work relating 
to the exploration of the local context (Cf. 4.4.1 
and 5.2.1) in two complementary directions. From 
the grammatical point of view, our exploration of 
the context is incomplete. For example, we do not 
categorise the unknown proper name in a complex 
case such as Its Belgian subsidiary specialising 
in flat products Nokia. From the semantic point 
of view, we do not use all the contextual data. For 
example, the sentence The company already serves 
Houston, Saint-Louis and Dallas should be suffi- 
cient to disambiguate Saint-Louis. We are cur- 
rently accumulating examples in which the local con- 
text enables certain proper names to be categorised 
and/or to be disambiguated. Our next step will con- 
sist in tightening cooperation with the following lay- 
ers in order to use the grammatical and semantic 
data they provide in the whole process. 
Aknowledgements 
We would like to thank Andr6 Blavier, Jean- 
Francois Perrot and Jean-Marie S6z6rat and the ref- 
erees for their comments on versions of this paper. 
29 
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