Adaptive Name Entity Recognition under Highly Unbalanced Data
Table 3: Accuracy of baseline methods using full training data with weighted loss function.
['[BOLD] Method', '[BOLD] 0-Class', '[BOLD] 1-Class']
To overcome this issue, we introduce a modified binary loss function that exploits the whole data for training procedure: Lwce=−p0t0log(s0)−p1t1log(s1) (7) where (t0,s0),(t1,s1) denote for the ground truth and scores for class 0 and 1, respectively. p0,p1 denote for the occurrence probability of class 0 and 1 in the training data. In our experiment, the p0≈0.02 and p1≈0.98. It is really interested when the RNN-CNN improves the accuracy on both 0 and 1 class with a large margin compared two remaining methods, holding 75% on ”1” class and 99.6% on ”0” class. This improvement can be reasoning as the effect of setting small weights for class 0 and large weights for class 1. It forces the model to damp loss values when predict correctly ”0” class and amplifying error for incorrect assignment on ”1” class.