Unni Cathrine Eiken February 2005

Intuitively, this is a quite good association of concepts, since all the entities in the grouping
belong to the group law enforcement. If a person were to group nominals from the text
collection into semantically similar concept classes, the grouping in Figure 6 would not be an
unlikely result. The grouping as shown in Figure 6, however, is the result of an association
based on context information from the text itself.
4.2.1 Classify
Manually performing the association method as described above on all the EPAS in the data set
proved to be bordering on the impossible, mainly because it implied consulting the data set
multiple times, each time looking for different values and keeping track of the partial goals in
the process. Based on the method as described above, the Perl script classify was written 5 . In the
following, the algorithm implemented in Classify is outlined in brief.
For each predicate:
1. Level 0:
What is ARG1 and ARG2 in the corpus/EPAS list?
2. Level 1:
For each ARG1 = x that was found in 1:
In connection with which other predicates is ARG1 also= x?
For each of these predicates:
Which other words occur as ARG1?
Produces a list of words which occur in the same contexts as x
3. Level 2:
For each word = y in the list from level 1:
Which other predicates does this word also co-occur with?
For each of these predicates:
Which other words occur as ARG1?
Produces a list of words which occur in the same contexts as y
Same procedure is repeated for ARG2.
5
The algorithm was implemented in Perl by Martin Rasmussen Lie, informatics student at the University of
Bergen.
72