Unni Cathrine Eiken February 2005

4 Classification
In order to use the structures in the EPAS list as an aid in anaphora resolution, they have to be
processed. The pre-processing in section 3.6.4 has shown that there does exist interesting
distributions in the data set and indicates that certain groups of arguments display distributions
particular for the domain. As a step toward exploring if these distributions can be used to
represent selectional restrictions and thus function as real-world knowledge for the domain, the
words in the EPAS list must be classified. This procedure uses the context pattens that a word
occurs in to classify the word, for example allowing for an argument to be classified according
to the predicates it co-occurs with. A classification of this type gives information about which
word to expect in a given context pattern and the results can therefore be used in the process of
chosing the most likely antecedent for an anaphor. In this respect, the most likely antecedent
must be interpreted as the most likely antecedent given a particular contextual pattern.
In the following, the EPAS list will first be classified to see if the context patterns represented
by the EPAS contain enough information to suggest the correct antecedent for anaphoric
expressions from the text collection. Then an association of concepts will be performed, creating
bundles of those arguments which occur in similar contexts/with similar predicates. These
concepts will then be applied in co-occurrence with the classification method to see if they
improve the process of suggesting the correct antecedent for the anaphors.
For the purposes of classification and testing, the EPAS list was divided into training and test
sets. The test set consist of all structures containing pronouns, while the training set consists of
the remaining EPAS. In the case of the test set, the correct antecedent for each pronoun was
identified manually and added to the test file. When testing with the test instances, the classifier
assigns an antecedent based on the patterns it has seen in the training set. In this way, the correct
antecedent in each test case functions as a means of measuring the success rate of the
classification. The test set provides a good way of testing the product of the classification and
gives a measure as to whether the correct antecedent can be assigned based on training on
occurrences of EPAS/context patterns.
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