Conditional Random Fields are commonly trained to maximize likelihood. The corresponding Fβ measure, the weighted harmonic mean of preci-sion and recall, which is established for evaluation in information retrieval and text mining, is not necessarily the optimal result for the user’s choice of β.
Some approaches have been published to optimize multivariate measures like Fβ to overcome this inconsistency. The limitation is that constraints like the value of β have to be known at training time.
This publication proposes a method of multiobjective optimization of both precision and recall based on a preceding likelihood training. The output is an estimation of pareto-optimal solutions from which the user can select the best for the actual application. Evaluated on two publicly available data sets in the field of named entity recognition, nearly all Fβ values are superior to those resulting from log-likelihood training