A Linguistically Based Semantic Bias for Theory Revision
نویسنده
چکیده
We present a new approach to theory revision that uses a linguistically based semantics to help detect and correct errors in classification rules. The idea is that preferring linguistically cohesive revisions will enhance the comprehensibility and ultimately the accuracy of rules. We explain how to associate terms in the rules with elements in a lexical class hierarchy and use distance within the hierarchy to estimate linguistic cohesiveness. We evaluate the utility of this approach empirically using two relational domains.
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