Identifier Labeling Using Graphical Models
نویسندگان
چکیده
In this paper, we apply Bayesian Networks to the labeling of arbitrary string identifiers from search results over a music database. We find that our models perform with a 58% labeling accuracy, with errors primarily occurring when labeling string data not been seen during training. We also present a method for searching potential labelings which attempts to address the exponential blow up of the labeling permutation space.
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