bert bigru self-attention模型 bert bigru self-attention模型BERT（Bidirectional Encoder Representations from Transformers）是一个双向的自注意力（self-attention）模型，它采用 Transformer 结构进行预训练，广泛用于自然语言处理（NLP）任务。GRU（Gated Recurrent Unit）是一种循环神经网络（RNN）的变种，用于处理序列...
《地质通报》网络首发论文 题目: 结合 BERT 与 BiGRU-Attention-CRF 模型的地质命名实体识别 作者: 谢雪景,谢忠,马凯,陈建国,邱芹军,李虎,潘声勇,陶留锋 网络首发日期: 2021-09-13 引用格式: 谢雪景,谢忠,马凯,陈建国,邱芹军,李虎,潘声勇,陶留锋.结合 BERT与 BiGRU-Attention-CRF 模型的地质命名实体识别....
针对网络安全这一特殊背景[1],设计了一种BERT-BiGRU-Self-Attention-CRF模型进行命名实体识别。以预训练模型Bert作为底座,通过Bert提升模型的语义理解和句子表达能力;结合双向的门控循环单元BiGRU,通过前向和后向传播来融合句子中的上下文信息,更好地捕捉前后文之间的关联特征;将BiGRU层的输出输入注意力机制中[2],通过...
Therefore, based on the pre-trained model (BERT), this paper proposes a military event detection method which combines BiGRU and attention mechanism. The language model is trained to construct a vector representation method combining word vector and position vector. BiGRU neural network is used to ...
针对该问题,提出一种基于BERT的BiGRU-Attention-CNN混合神经网络模型的中文情感分析方法.BERT模型能产生丰富的动态词向量,结合BiGRU对上下文的长期依赖能力和CNN的特征提取能力,并融入Attention机制分配不同的权重值重点关注.在酒店评论,外卖评论,网购评论,微博评论四种公开中文数据集进行情感分类实验,实验结果表明,该模型相...
[25] 预训练模型获得文本的上下文特征表示,使用密集 连接的双向GRU网络进一步提取农业问句对的文 本特征,使用连接操作将注意力机制对2个问句交 互的信息合并到密集连接的BiGRU中用于问句相 似度匹配,并进一步针对神经网络的重要参数进行 优化和改进,提出基于BERT - Attention DenseBiGRU的农业文本相似度匹配模型,以期...
Our experiments show that our proposed hybrid neural network model SolBERT-BiGRU-Attention is fitted by a large number of data samples with smart contract vulnerabilities, and it is found that compared with the existing methods, the accuracy of our model can reach 93.85%,...
Chinese Text Sentiment Analysis Based on BERT-BiGRU Fusion Gated AttentionHuang ShufenLiu ChanghuiZhang Yinglin
Chinese Text Sentiment Analysis Based on BERT-BiGRU Fusion Gated AttentionHuang ShufenLiu ChanghuiZhang Yinglin
In BBGCAP, the unstructured text data are preprocessed vectorically using BERT, BiGRU is used to extract the deep features of the text, attention pooling is used to assign the corresponding weights to the extracted deep information, and CapsNet is used to route the right ...