Unni Cathrine Eiken February 2005

When testing on knowledge-dependent anaphors and on anaphors which do not have an
explicitly mentioned antecedent in the text, it was evident that concept groups contribute with
interesting information. Ideally, a referring guessing helper using concept groups should be
consulted as part of an anaphora resolution system. In the event of several possible antecedent
candidates motivated from the text and proposed by the system, the concept groups in
connection with the context pattern of the anaphor can provide useful information about which
type of antecedent is likely. In this way the concept groups resemble information about the valid
contextual patterns for the domain.
The stumbling block of the method in this thesis is the limited dimension of it. The data set used
for the analyses is fairly small and as a consequence the results are less powerful than they could
have been. The extraction method is at best semi-automatic and employs far too much manual
intervention. This is a reoccurring problem for many methods within the field of NLP, Mitkov
for example says that “only a few anaphora resolution systems operate in fully automatic mode”
(Mitkov 2001, p. 111). Most systems rely on manual pre-editing of the input texts, some
methods are only manually simulated. In order for a method to be fully automatic there should
be no human intervention at any stage (Mitkov 2001, p. 114). In the case of the project described
in this thesis, the extraction method is far too manually manipulated to be considered automatic.
The scope of the results is naturally influenced by the limitations of the data set, but regardless
of the size of the data set and the manual intervention employed in the extraction phase, the
method shows promising results. It was clear from the beginning that this would be a pilot study
aiming at providing an indication of the usefulness of the method.
In view of the results, it should be stated that using contextual distribution to derive intuitions of
selectional restrictions in a limited domain is a promising venture. The results obtained in this
project suggest that the distribution of predicates and arguments within a closed domain has a
potential use as a representation of real-world knowledge. More definite conclusions about the
extent to which such a method captures enough relevant intuitions about real-world knowledge
to replace it in an anaphora resolution system can, however, first be made in the event of a
larger-scale study.
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