Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Conditional Random Fields are commonly trained to maximize likelihood. The corresponding F measure, the ighted harmonic mean of precision 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 . Some approaches have been published to optimize multivariate measures like F to overcome this inconsistency. The limitation is that constraints like the value of 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 F values are superior to those resulting from log-likelihood training.
['Roman Klinger', 'Christoph M. Friedrich']
I am writing a research paper on a idea User's Choice of Precision and Recall in Named Entity Recognition
[{"from": "human", "value": "I am writing a research paper on a idea User's Choice of Precision and Recall in Named Entity Recognition"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Conditional Random Fields are commonly trained to maximize likelihood. The corresponding F measure, the ighted harmonic mean of precision and recall, which is established for evaluation in information retrieval and text mining, is not necessarily the optimal result for the user\u2019s choice of . Some approaches have been published to optimize multivariate measures like F to overcome this inconsistency. The limitation is that constraints like the value of 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 F values are superior to those resulting from log-likelihood training."}]