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Abstract:
If the promise of computational modeling is to be fully
realized in higher-level cognitive domains such as language
processing, principled methods must be developed to construct the
semantic representations used in such models. In this paper, we
propose the use of an established formalism from mathematical
psychology,
additive clustering
, as a means of automatically constructing binary
representations for objects using only pairwise similarity data.
However, existing methods for the unsupervised learning of
additive clustering models do not scale well to large problems.
We present a new algorithm for additive clustering, based on a
novel heuristic technique for combinatorial optimization. The
algorithm is simpler than previous formulations and makes fewer
independence assumptions. Extensive empirical tests on both human
and synthetic data suggest that it is more effective than
previous methods and that it also scales better to larger
problems. By making additive clustering practical, we take a
significant step toward scaling connectionist models beyond
hand-coded examples.
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