Paper: A Maximum Expected Utility Framework for Binary Sequence Labeling

ACL ID P07-1093
Title A Maximum Expected Utility Framework for Binary Sequence Labeling
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2007
Authors

We consider the problem of predictive infer- ence for probabilistic binary sequence label- ing models under F-score as utility. For a simple class of models, we show that the number of hypotheses whose expected F- score 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.