A Maximum Expected Utility Framework for Binary Sequence Labeling
Martin Jansche · Meeting of the Association for Computational Linguistics · 2007
We consider the problem of predictive inference for probabilistic binary sequence labeling models under F-score as utility. For a simple class of models, we show that the number of hypotheses whose expected Fscore needs to be evaluated is linear in the sequence length and present a framework for efficiently evaluating the expectation of many common loss/utility functions, including the F-score. This framework includes both exact and faster inexact calculation methods.