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<?xml version="1.0" standalone="yes"?> <Paper uid="E95-1004"> <Title>Automatic Processing of Proper Names in Texts</Title> <Section position="2" start_page="0" end_page="23" type="metho"> <SectionTitle> 2 Problems raised by proper names in NLP 1 Introduction </SectionTitle> <Paragraph position="0"> The Exoseme system \[6, 7\] is an operational application which continuously analyses the economic flow from Agence France Presse (AFP). AFP, which covers the current economic life of the major industrialised countries, transmits on average 400 dispatches per day on this flow. Their content is drafted in French in a journalistic style. Using this flow, Exoseme feeds various users concerning precise and varied subjects, for example, rating announcements, company results, acquisitions, sectors of activity, observation of competition, partners or clients, etc. 50 such themes have currently been developed. They rely on precise filtering of dispatches with highlighting of sentences for fast reading.</Paragraph> <Paragraph position="1"> Exoseme is composed of several modules : a morphological analyser, a proper name module, a syntactical 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 during the semantic analysis. Third, it is invoked by the filtering module to supply some more information needed for routing the dispatch.</Paragraph> <Paragraph position="2"> 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 significant part of the text. They account for approximately 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 infinite. null 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 (individual, company, location, etc.). It should be noted that segmentation and categorisation are processed differently depending on whether the proper name is known or unknown.</Paragraph> <Section position="1" start_page="0" end_page="23" type="sub_section"> <SectionTitle> 2.1 Segmentation of proper names </SectionTitle> <Paragraph position="0"> 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, conjunctions, commas, full stops). As illustrated in \[4\], segmentation firstly prevents long proper names from undertaking pointless analyses. For example, for Caisse de C/r6dit hgricole du Morbihan the analyser will provide two interpretations depending on whether Morbiha.n is attached to Cr6dit hgricole or to Caisse. Moreover, proper names often constitute agrammatical segments that sometimes confuse the synctactical analyser. For exam- null ple, in the sentence The director of Dollfus, Mieg and Cie has announced positive results, the analyser 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.</Paragraph> <Paragraph position="1"> Segmentation of known proper names has already been studied and is treated in some systems such as NameFinder \[5\]; segmentation of unknown proper na,nes based on pattern matching is implemented in several systems \[1, 2, 4, 9\]; the morphological matching of acronyms is described in \[11\].</Paragraph> </Section> <Section position="2" start_page="23" end_page="23" type="sub_section"> <SectionTitle> 2.2 Categorisation of proper names </SectionTitle> <Paragraph position="0"> Once the segmentation has been achieved, categorisation of proper names is necessary for the semantic analyse> Categorisation maps proper names into a set of concepts (e.g. human being, company, location). The very nature of proper names contributes 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 acquisitions, 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 sentence, which is grammatically identical to the preceding one, Compagnie des Signaux intends to sell TVM430 for 700 MF indicates only a price for an industrial product.</Paragraph> <Paragraph position="1"> Categorisation of unknown proper names has already been studied as well. Particularly, categorisation 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 implemented in \[2, 9\].</Paragraph> <Paragraph position="2"> In our system, these ontological categories are extended to attributes needed by the semantic analyser or the filtering module. For instance, proper names may have different attributes such as city, rating agencies, sector of activity, market, financial indexes, etc.</Paragraph> </Section> </Section> <Section position="3" start_page="23" end_page="23" type="metho"> <SectionTitle> 3 Representation of proper </SectionTitle> <Paragraph position="0"> 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 information 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 representation system similar to conceptual graphs \[10\], the flexibility of which effectively gives expressiveness, reusability and the possibility of further development. It enables indispensable and heterogeneous data to be memorised and used in order to process proper names.</Paragraph> <Paragraph position="1"> For a given proper name, its category and its various 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 encountered problem uses the information of tile knowledge base and may add its own information to it.</Paragraph> <Paragraph position="2"> The final result is a large knowledge base including 8,000 proper names sharing 10,000 fornas, based on 11,000 words. There are also 90 attributes of proper names or words. Each new filtering theme may be a special case and its implementation may lead to introduce additional attributes into the knowledge base. The adopted representational formalism enables these additions to be made without leading to substantial modifications of its structure.</Paragraph> </Section> <Section position="4" start_page="23" end_page="28" type="metho"> <SectionTitle> 4 Processing known proper </SectionTitle> <Paragraph position="0"> 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 processes. We also seek to recognise the most frequent proper names (e.g. country, cities, regions, statesmen) in order to segment them and categorise them correctly.</Paragraph> <Section position="1" start_page="23" end_page="25" type="sub_section"> <SectionTitle> 4.1 Immediate recognition </SectionTitle> <Paragraph position="0"> 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.</Paragraph> <Paragraph position="1"> All this information is stored in the knowledge base which contains, for example :</Paragraph> <Paragraph position="3"> --+ rating agency The knowledge base is thus structured on the model showed in Figure 2. And subsequently, recognition of the proper name in the text occurs through simple pattern matching.</Paragraph> <Paragraph position="5"/> <Paragraph position="7"/> </Section> <Section position="2" start_page="25" end_page="25" type="sub_section"> <SectionTitle> 4.2 &quot;Equivalent&quot; words </SectionTitle> <Paragraph position="0"> However, words lnaking up proper names accept.</Paragraph> <Paragraph position="1"> many slippages which result from abbreviat, ions, translation, common faults, etc. For example : 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 authorised. To this end, slippages in a given word are grouped around an &quot;equivalent&quot;. This technique, which has been developed in the NameFinder system \[5\], under the term &quot;alternative&quot; words, enables to make a correspondence with different forms likely to appear.</Paragraph> <Paragraph position="2"> Equivalent words are expressed in the knowledge base through a relationship. For example, our base contains the graph of Figure 3.</Paragraph> </Section> <Section position="3" start_page="25" end_page="25" type="sub_section"> <SectionTitle> 4.3 Synonymous proper names </SectionTitle> <Paragraph position="0"> 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.</Paragraph> <Paragraph position="1"> 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 attributes 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.</Paragraph> </Section> <Section position="4" start_page="25" end_page="26" type="sub_section"> <SectionTitle> 4.4 Disambiguating proper names </SectionTitle> <Paragraph position="0"> 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 produces homonyms. Such interference, which is annoying for users, reflects the limitations of traditional keyword systems. In the AFP flow, for example, the form Saint-Louis may designate equally well: The crucial problem posed is to succeed in disambiguating this type of forms. Or, in other words, in determining, or at least in delimiting, the denoted reference.</Paragraph> <Paragraph position="1"> 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 delicate 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.</Paragraph> <Paragraph position="2"> We will see in section 5.2.1 that the local context may categorise an unknown proper name and therefore it may help to desambiguate an ambiguous known proper name. For instance, if the text speaks of the mayor of St-Louis, the company and hospital can certainly be ruled out.</Paragraph> <Paragraph position="3"> 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.</Paragraph> <Paragraph position="4"> 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. 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.</Paragraph> <Paragraph position="5"> Nevertheless, when the ambiguity is unable to be removed, we choose the most frequent interpretation, but the user is told of the doubtful nature of our choice. In the dispatch title &quot;Saint Louis: results up&quot;, 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.</Paragraph> <Paragraph position="6"> 5 Processing unknown proper names The preceding techniques tackled the problem of the variability of known proper names. However, although many proper names appear frequently, others appear only once. Even if the constituted knowledge base is very comprehensive, it is absolutely'impossible to record all potential proper names. We have therefore to deal with unknown proper names.</Paragraph> </Section> <Section position="5" start_page="26" end_page="26" type="sub_section"> <SectionTitle> 5.1 Prototypes of proper names </SectionTitle> <Paragraph position="0"> As fully explained in \[2\], some proper names are constructed according to prototypes which enable them to be categorised through their appearance alone.</Paragraph> <Paragraph position="1"> Furthermore, certain categories of proper names accept traditional extensions which it is also possible to detect. For example : company (e.g. Bernard Tapie Finance) Lastly, such extensions may be combined, e.g, &quot;Siam Nissan Automobile Co Ltd&quot; is probably a subsidiary of Nissan.</Paragraph> <Paragraph position="2"> 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.</Paragraph> <Paragraph position="3"> 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.</Paragraph> </Section> <Section position="6" start_page="26" end_page="28" type="sub_section"> <SectionTitle> 5.2 Other techniques of categorisa- </SectionTitle> <Paragraph position="0"> 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 ? 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.</Paragraph> <Paragraph position="1"> These rules once again require that certain words from the knowledge base are marked by individual attributes. For example, the word &quot;mayor&quot; has both the following attributes : * human-being-apposition : (e.g. Chirac, mayor of the town) * location-name-complement : (e.g. the mayor of Royan)</Paragraph> <Paragraph position="3"> text However, the local context of a proper name does not necessarily enable one to infer its category. For instance, the mere radical of a proper name (e.g. family 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.</Paragraph> <Paragraph position="4"> 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 establish 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 sometimes share the same radical : Mr Mitterand and Mrs Mitterand, or again Mr Bollor4 and Bollor6 Group.</Paragraph> <Paragraph position="5"> Although, in the most frequent cases, we resolve this well-known problem but as in \[11\] we do not have a general solution.</Paragraph> </Section> <Section position="7" start_page="28" end_page="28" type="sub_section"> <SectionTitle> 5.3 Matching acronyms </SectionTitle> <Paragraph position="0"> Acronyms occur frequently in AFP dispaches. On one hand, the linguistical construction of the corresponding text of acronyms may be relatively complex. 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 unthinkable 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 segment these unknown proper names and second desambiguate these potential homonylns.</Paragraph> <Paragraph position="1"> In general, when an acronym is introduced in a text, its complete form is given using parentheses.</Paragraph> <Paragraph position="2"> As observed in \[11\], it is possible to explore the local structure of the parentheses in order to determine 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 &quot;des&quot;, while the acronym BDF of Banque de France keeps the &quot;de&quot;. In order for our processing module to recognise these words, we allocate a special attribute to them in the knowledge base.</Paragraph> <Paragraph position="3"> This simple and effective technique enables most of the acronyms introduced to be processed correctly. Only foreign acronyms accompanied by their translation are not processed.</Paragraph> </Section> </Section> class="xml-element"></Paper>