Another form of association can be defined by taking as the baseline model the average
distribution of the conditioned category over all possible instantiations of the conditioning
category (in the formulation of the previous sub-section, x would range over all categories of the same type, rather than over all immediate siblings). This form is demonstrated in Figure E, which lists the strongest associations found between some
country and some topic. The system also enables the user to investigate further the
subset of documents which corresponds to a certain association. In Figure E we chose to
explore the set of documents corresponding to the association between South Korea and
trade, presenting the distribution of countries within this set (lower-right listbox, specified
by the “Expand Category” pull-down menu). This reveals which countries are most
prominent in articles dealing with both South Korea and trade, conveniently linking the
browsing mechanism of Figure C to the association display screen.
In many cases, the system generates a very large number of associations, making it
difficult to draw overall conclusions. To summarize the information, the system
groups together correlations whose second component belongs to the same class in the
hierarchy. Figure F shows the clusters that were formed by the system when grouping all
the individual associations of Figure E. For example, in 43 associations of Figure E the
right hand side of the association (the topic) was a daughter of the node agriculture. The user can examine any cluster and see the specific associations it contains (lower
listbox, for the selected cluster caffeinedrinks).
In addition, the system tries to provide a compact generalization for all the categories on the left hand side of the associations in the cluster. In our example, the system found that all countries that are highly correlated with caffeine drinks belong either to the OAU (African Union) or the OAS (South American countries) organizations.
Figure F - Clustering associations using the category hierarchy. In the upper listbox we can see all association clusters that were formed by the system along with their sizes (in
parenthesis). In the lower listbox we see the members of the cluster that was selected in the upper listbox (caffeine drinks).
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distribution of the conditioned category over all possible instantiations of the conditioning
category (in the formulation of the previous sub-section, x would range over all categories of the same type, rather than over all immediate siblings). This form is demonstrated in Figure E, which lists the strongest associations found between some
country and some topic. The system also enables the user to investigate further the
subset of documents which corresponds to a certain association. In Figure E we chose to
explore the set of documents corresponding to the association between South Korea and
trade, presenting the distribution of countries within this set (lower-right listbox, specified
by the “Expand Category” pull-down menu). This reveals which countries are most
prominent in articles dealing with both South Korea and trade, conveniently linking the
browsing mechanism of Figure C to the association display screen.
In many cases, the system generates a very large number of associations, making it
difficult to draw overall conclusions. To summarize the information, the system
groups together correlations whose second component belongs to the same class in the
hierarchy. Figure F shows the clusters that were formed by the system when grouping all
the individual associations of Figure E. For example, in 43 associations of Figure E the
right hand side of the association (the topic) was a daughter of the node agriculture. The user can examine any cluster and see the specific associations it contains (lower
listbox, for the selected cluster caffeinedrinks).
In addition, the system tries to provide a compact generalization for all the categories on the left hand side of the associations in the cluster. In our example, the system found that all countries that are highly correlated with caffeine drinks belong either to the OAU (African Union) or the OAS (South American countries) organizations.
Figure F - Clustering associations using the category hierarchy. In the upper listbox we can see all association clusters that were formed by the system along with their sizes (in
parenthesis). In the lower listbox we see the members of the cluster that was selected in the upper listbox (caffeine drinks).
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