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<Paper uid="C94-1100">
  <Title>BUILDING A LEXICAL DOMAIN MAP FROM TEXT CORPORA</Title>
  <Section position="8" start_page="607" end_page="607" type="concl">
    <SectionTitle>
CONCLUSIONS
</SectionTitle>
    <Paragraph position="0"> We discussed selected aspecLq our inlormation retrieved system consisting of an advanced NLP module and a 'st~mdard' statistical core engine, ht this paper we concentrated on the problem of automatic generation of lexical correlations among terms which (aloug with appropriate weighting scheme) represent the content of both the dat:d)ase documents :rod the user queries. Since it successful retrieval relies on actual term matches between the queries ,'u~d the documents, it is essential tlmt any lexical alternatives of describing a given topic ,are taken into account. In our system this is achieved through the expansion of user's queries with related terms: we add equiwdent ,and more specific terms. Lexical relations between terms are c;dculated directly from the database and stored in tbe form of a dom~dn map, which thus acts as a domaln-specilic thesaurus. Query expansion can be done in the user-feedback mode (with user's assistance) or automatically. In this latter c~se, local context is explored to ,assure meaningful exp~msious, i.e., to prevent e.g., exp,'mding 'charge' with 'expense' when 'allege' or 'blame' is meant, as in the following ex~unple query: Documents will report on corruption, incompetence, on' inefficiency in the m.magement of the United N.'~litm's st'dT. Alleg~dions t~l' lnIil|agelnelll railings, as well as Felofls Io StlCh charges ~u'e relevanl.</Paragraph>
    <Paragraph position="1"> Many problems remain, however, we attempted 1o demonstrate that the architecture described here is nonetbeless viable and h`as practiced significance. More advanced NLP techniques (including semantic ,'m~dysis) may prove to be still more effective, in the future, however their enormous cost limits ~my experimental evidence to small scale tests (e.g., Mauldin, 1991).</Paragraph>
  </Section>
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