Subjects: Computer Science >> Natural Language Understanding and Machine Translation submitted time 2022-07-19
Abstract： when there are few labeled data, the existing models are limited by the amount of training data, and the parameters do not fit the expected effect, resulting in poor model recognition performance in the task of low resource named entity recognition. a new loss function integrated with Bernoulli distribution is proposed to make the model fit the data better. In addition, based on the BiLSTM-CRF model, this paper integrates multi-layer character feature information and self attention mechanism, and the new loss function based on Bernoulli distribution is combined to construct the BiLSTM-Att-BCRF model. Based on the dataset of 20% CONLL2003 and 20% BC5CDR, the F1 value of the BiLSTM-BCRF model proposed in this paper increased by 7.00% and 4.08% respectively. the model can better adapt to the task of low resource named entity recognition.
Peer Review Status:
Awaiting Review
Subjects: Computer Science >> Other Disciplines of Computer Science submitted time 2022-01-02
Abstract： " [Objective]when there are few labeled data, the existing models are limited by the amount of training data, and the parameters do not fit the expected effect, resulting in poor model recognition performance in the task of low resource named entity recognition. [Methods] a new loss function integrated with Bernoulli distribution is proposed to make the model fit the data better. In addition, based on the BiLSTM-CRF model, multi-layer character feature information is fused, and the new loss function based on Bernoulli distribution is combined to construct the BiLSTM-BCRF model. [Results] Based on the dataset of 20% CONLL2003 and 20% BC5CDR, the F1 value of the BiLSTM-BCRF model proposed in this paper increased by 6.16% and 3.35% respectively. [Conclusion] the model can better adapt to the task of low resource named entity recognition. [Limitations] the performance of this model in identifying proper nouns needs to be improved
Peer Review Status:Awaiting Review
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