Automatic Processing of Proper Names in Texts

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. …

Content truncated to 500 words