.png){kind=icon}
返回
Why KDAC? A general activation function for knowledge discovery
.png){kind=icon}
OA
AI
DOI：10.1016/j.neucom.2022.06.019.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Deep learning oriented named entity recognition (DNER) has gradually become the paradigm of knowledge discovery, which greatly promotes domain intelligence. However, the activation function of DNER fails to treat gradient vanishing, no negative output or non-differentiable existence, which may impede the exploration of knowledge due to the omission and incomplete representation of the latent semantic. To break through the dilemma, we present a novel activation function termed KDAC. Detailly, KDAC is an aggregation function with multiple conversion modes. The backbone is the interaction between exponent and linearity, and the both ends are extended through adaptive linear divergence, which can surmount the gradient vanishing and no negative output. Crucially, the non-differentiable points can be alerted and eliminated by an approximate smoothing algorithm. KDAC has a series of brilliant properties, such as nonlinear, stable near-linear transformation and derivative, as well as dynamic style, etc. We perform experiments based on BERT-BiLSTM-CNN-CRF model on six benchmark datasets containing different domain knowledge, such as Weibo, Clinical, E-commerce, Resume, HAZOP and People's daily. The evaluation results show that KDAC is advanced and effective, and can provide more generalized activation to stimulate the performance of DNER. We hope that KDAC can be exploited as a promising activation function to devote itself to the construction of knowledge. (c) 2022 Elsevier B.V. All rights reserved.
Keyword:
KDAC
Knowledge discovery
DNER
Activation function
Approximate smoothing algorithm
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
Enhanced sequence labeling based on latent variable conditional random fields.png)
NEUROCOMPUTINGOAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏Improving named entity recognition in noisy user-generated text with local distance neighbor featureNEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Identifying adverse drug reaction entities from social media with adversarial transfer learning model利用对抗性迁移学习模型从社交媒体中识别药物不良反应实体NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Named entity recognition for extracting concept in ontology building on Indonesian language using end-to-end bidirectional long short term memory使用端到端双向长短期记忆在印尼语本体构建中提取概念的命名实体识别
PREAI
分享收藏
查看更多内容