Word association norms, mutual information, and lexicography
Computational Linguistics
Class-based n-gram models of natural language
Computational Linguistics
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In this paper we discuss the results of experiments which use a context, essentially an ordered set of lexical items, as the seed from which to build a network representing statistically important relationships among lexical items in some corpus. A metric is then applied to the nodes in the network in order to discover those pairs of items related by high indices of similarity. The goal of this research is to instantiate a class of items corresponding to each item in the priming context. We believe that this instantiation process is ultimately a special case of abstraction over the entire network; in this abstraction, similar nodes are collapsed into metanodes which may then function as if they were single lexical items.