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Before going on to consider WordNet,another elaborate thesaurus, it is
useful to relate these , manually-constructed representations with
automatic, statistically-derived analogs. Such comparisons have been
part of IR research since its beginnings [Joyce58] [Dennis64] [Soergel74] . Section §5.2.5 has discussed how the same
information used to cluster documents can be used with keywords as well.
The semantics of relations based strictly on co-occurrence frequencies
are not obvious [vanR77] , but seem to
provide evidence for the \textbf{RT} (related term, or synonymy
relation) discussed above.
Using this information to construct hierarchic
relations among keywords corresponds to (hierarchic) clustering
techniques. Thesaurus-specific techniques generally exploit a heuristic
that high frequency keywords correspond to broad, general terms while
low frequency keywords correspond to narrow, specific ones [Srinivasan92] . This heuristic can
be used to organize keywords into levels of a taxonomy, with the
hierarchic parent/child relation formed between those keywords with
similar document distributions. RelFbk can also be used to provide
theaurus structure [Guntzer89] .
Whether constructed manually or automatically, thesuarus structures
support many new forms of naviagation [REF1077] [REF1079] .
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Automatic thesaurus construction