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        <title>基于BiLSTM聚合模型的汉语框架语义角色识别(Chinese Frame Semantic Role Identification Based on BiLSTM Aggregation Model)</title>
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            <title>Proceedings of the 22nd Chinese National Conference on Computational Linguistics</title>
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    <abstract>“目前,基于神经网络的汉语框架语义角色识别模型的性能依然较低,考虑到神经网络模型的性能受到超参数的影响,本文将超参数调优和模型预测性能的提升统一到基于BiLSTM的聚合模型框架下解决。使用正则化交叉验证进行实验,通过正则化条件约束训练集和验证集的分布差异,避免分布不一致带来的性能波动。将交叉验证得到的结果进行众数投票,以投票后的结果对不同的超参数配置进行评估,并选择若干种没有显著差异的超参数配置构成最优的超参数配置集合。然后将最优的超参数配置集合对应的子模型进行聚合,构造汉语框架语义角色识别的聚合模型。实验结果显示,本文方法的性能较基准模型显著提升了9.56%。”</abstract>
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%0 Conference Proceedings
%T 基于BiLSTM聚合模型的汉语框架语义角色识别(Chinese Frame Semantic Role Identification Based on BiLSTM Aggregation Model)
%A Cao, Xuefei
%A Li, Hongji
%A Wang, Ruibo
%A Niu, Qian
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G Chinese
%F cao-etal-2023-ji
%X “目前,基于神经网络的汉语框架语义角色识别模型的性能依然较低,考虑到神经网络模型的性能受到超参数的影响,本文将超参数调优和模型预测性能的提升统一到基于BiLSTM的聚合模型框架下解决。使用正则化交叉验证进行实验,通过正则化条件约束训练集和验证集的分布差异,避免分布不一致带来的性能波动。将交叉验证得到的结果进行众数投票,以投票后的结果对不同的超参数配置进行评估,并选择若干种没有显著差异的超参数配置构成最优的超参数配置集合。然后将最优的超参数配置集合对应的子模型进行聚合,构造汉语框架语义角色识别的聚合模型。实验结果显示,本文方法的性能较基准模型显著提升了9.56%。”
%U https://aclanthology.org/2023.ccl-1.38
%P 434-443

Markdown (Informal)

[基于BiLSTM聚合模型的汉语框架语义角色识别(Chinese Frame Semantic Role Identification Based on BiLSTM Aggregation Model)](https://aclanthology.org/2023.ccl-1.38) (Cao et al., CCL 2023)

• 基于BiLSTM聚合模型的汉语框架语义角色识别(Chinese Frame Semantic Role Identification Based on BiLSTM Aggregation Model) (Cao et al., CCL 2023)

ACL

• Xuefei Cao, Hongji Li, Ruibo Wang, and Qian Niu. 2023. 基于BiLSTM聚合模型的汉语框架语义角色识别(Chinese Frame Semantic Role Identification Based on BiLSTM Aggregation Model). In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 434–443, Harbin, China. Chinese Information Processing Society of China.