Unni Cathrine Eiken February 2005

The process of classifying the constituents of the EPAS is most useful if the aim of the
classification is held clearly in mind. Classifying arguments relative to the predicates and the
other arguments they co-occur with can give information about two things;
• is the data set generalisable enough to allow inference of the single correct antecedent in
each test case?
• is the data set generalisable enough to allow inference of words within the semantic
concept group that the correct antecedent belongs to?
In this thesis, it is of interest to identify all the words which occur in specific environments. As a
reaction, we are interested in finding the members which can co-occur in a specific pattern – and
not necessarily only in the single correct antecedent.
The classification phase in the present work has three steps; firstly classification through a
memory-based learning algorithm, secondly association of semantic classes from the text
material by looking at contextual environments, and thirdly classification through application of
the concept groups gathered in step two. In the following, the classification method will be
described in more detail.
4.1 Step I: Classification with TiMBL
TiMBL (Tilburg Memory Based Learner) (Daelemans et al. 2003) is a memory-based learning
(MBL) tool developed by the ILK research group at the University of Tilburg (ILK 2004).
TiMBL has been developed with the domain of NLP specifically in mind and provides an
implementation of several MBL algorithms.
Within MBL, or lazy learning (Daelemans et al. 1999), training instances are simply stored in
memory. Upon encountering new instances, classification is performed by comparing the new
instance to the stored experiences and estimating the similarity of the new instance to the old
ones. The stored example(s) most similar to the new instance is picked as its classification. This
approach stands in opposition to rule-induction based methods, which also are called greedy
algorithms. In greedy learning algorithms, the learning material is used to create a model with
expected characteristics for each category to be learned. Daelemans et al. (1999) show that
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