A deep learning-based information extraction algorithm (named BERT-BiLSTM-CRF) for automatically extracting temporal information from social media messages is proposed. Based on the bidirectional long short-term memory-conditional random field (BiLSTM-CRF) model, the BERT (bidirectional encoder ...
1) 模型结构 Wu S , He Y . Enriching Pre-trained Language Model with Entity Information for Relation Classification[J]. 2019. 如上图所示,是R-BERT的模型结构在模型中,需要注意一下三点: 1.为了使BERT模型能够定位两个实体的位置,作者在每个句子的开头添加 "[CLS]" ,在第一个实体前后添加特殊字符 "...
在自然语言处理和知识图谱中,实体抽取、NER是一个基本任务,也是产业化应用NLP 和知识图谱的关键技术之一。BERT是一个大规模预训练模型,它通过精心设计的掩码语言模型(Masked Language Model,MLM)来模拟人类对语言的认知,并对数十亿个词所组成的语料进行预训练而形成强大的基础语义,形成了效果卓绝的模型。通过 BERT来进...
本文涉及的模型参考论文如下: [1] Wu S , He Y . Enriching Pre-trained Language Model with Entity Information for Relation Classification[J]. 2019. [2] Giorgi J , Wang X , Sahar N , et al. End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models[J]....
and fuses the outputs of different layers of decoding layers to achieve fine extraction of multi-layer semantic information. The experimental results show that the ConTextBERT-CNN model achieves classification accuracies of 86.4%, 82.0%...
[1] Wu S , He Y . Enriching Pre-trained Language Model with Entity Information for Relation Classification[J]. 2019. [2] Giorgi J , Wang X , Sahar N , et al. End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models[J]. 2019. ...
[1] Wu S , He Y . Enriching Pre-trained Language Model with Entity Information for Relation Classification[J]. 2019. [2] Giorgi J , Wang X , Sahar N , et al. End-to-end Named Entity Recognition and Relation Extraction using Pre-trained Language Models[J]. 2019. ...
Fig. 1. Model architecture: BERT + Bi-LSTM + CRF. 3.5. Dictionary and radical features We propose a post-processed way to use dictionary information. More specifically, on the basis of terminology dictionaries (e.g. drug dictionary, surgery dictionary), we can find the corresponding entities ...
Wu S , He Y . Enriching Pre-trained Language Model with Entity Information for Relation Classification[J]. 2019. 如上图所示,是R-BERT的模型结构在模型中,需要注意一下三点: 1.为了使BERT模型能够定位两个实体的位置,作者在每个句子的开头添加 "[CLS]" ,在第一个实体前后添加特殊字符 "$" ,在第二...
This change enhances the model’s adaptability in processing positional information. Next, the study incorporates a dynamic weighted fusion pooling strategy25,26,27 that combines average pooling, max pooling, and self-attention pooling. This approach improves feature extraction and helps capture critical...