中文电子病历命名实体识别主要是研究电子病历病程记录文书数据集,文章提出对医疗手术麻醉文书数据集进行命名实体识别的研究。利用轻量级来自Transformer的双向编码器表示(A Lite Bidirectional Encoder Representation from Transform...中文电子病历命名实体识别主要是研究电子病历病程记录文书数据集,文章提出对医疗手术麻醉文书数据集进行命名实体识别的研究。利用轻量级来自Transformer的双向编码器表示(A Lite Bidirectional Encoder Representation from Transformers,ALBERT)预训练模型微调数据集和Tranfomers中的trainer训练器训练模型的方法,实现在医疗手术麻醉文书上识别手术麻醉事件命名实体与获取复杂麻醉医疗质量控制指标值。文章为医疗手术麻醉文书命名实体识别提供了可借鉴的思路,并且为计算复杂麻醉医疗质量控制指标值提供了一种新的解决方案。展开更多
While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning me...While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.展开更多
Background:The medical records of traditional Chinese medicine(TCM)contain numerous synonymous terms with different descriptions,which is not conducive to computer-aided data mining of TCM.However,there is a lack of m...Background:The medical records of traditional Chinese medicine(TCM)contain numerous synonymous terms with different descriptions,which is not conducive to computer-aided data mining of TCM.However,there is a lack of models available to normalize synonymous TCM terms.Therefore,construction of a synonymous term conversion(STC)model for normalizing synonymous TCM terms is necessary.Methods:Based on the neural networks of bidirectional encoder representations from transformers(BERT),four types of TCM STC models were designed:Models based on BERT and text classification,text sequence generation,named entity recognition,and text matching.The superior STC model was selected on the basis of its performance in converting synonymous terms.Moreover,three misjudgment inspection methods for the conversion results of the STC model based on inconsistency were proposed to find incorrect term conversion:Neuron random deactivation,output comparison of multiple isomorphic models,and output comparison of multiple heterogeneous models(OCMH).Results:The classification-based STC model outperformed the other STC task models.It achieved F1 scores of 0.91,0.91,and 0.83 for performing symptoms,patterns,and treatments STC tasks,respectively.The OCMH method showed the best performance in misjudgment inspection,with wrong detection rates of 0.80,0.84,and 0.90 in the term conversion results for symptoms,patterns,and treatments,respectively.Conclusion:The TCM STC model based on classification achieved superior performance in converting synonymous terms for symptoms,patterns,and treatments.The misjudgment inspection method based on OCMH showed superior performance in identifying incorrect outputs.展开更多
Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally intelligentmachines.Graph-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC tasks.However,their...Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally intelligentmachines.Graph-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC tasks.However,their limited ability to collect and acquire contextual information hinders their effectiveness.We propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address this.The proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human emotions.Themodel used text augmentation techniques to producemore training data,improving the proposed model’s accuracy.Transformer encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual information.This integration improves the accuracy and robustness of the proposed model.Furthermore,we present a method for balancing the training dataset by creating enhanced samples from the original dataset.By balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed model.Experimental results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ERC.TA-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based encoding.The balanced dataset and the additional training samples also enhance its resilience.These findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.展开更多
The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Curr...The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Current approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent summaries.This study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical information.Our approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text structures.This model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and Multi-News.The results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document contexts.This research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.展开更多
Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online spaces.To tackle this challenge,our study introduces a ...Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online spaces.To tackle this challenge,our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers(BERT)base model(cased),originally pretrained in English.This model is uniquely adapted to recognize the intricate nuances of Arabic online communication,a key aspect often overlooked in conventional cyberbullying detection methods.Our model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media(SM)tweets showing a notable increase in detection accuracy and sensitivity compared to existing methods.Experimental results on a diverse Arabic dataset collected from the‘X platform’demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods.E-BERT shows a substantial improvement in performance,evidenced by an accuracy of 98.45%,precision of 99.17%,recall of 99.10%,and an F1 score of 99.14%.The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications,offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.展开更多
Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requir...Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.展开更多
针对传统情感分析任务中,使用Word2Vec(word to vector)模型生成文本词向量无法有效解决多义词表征,经典神经网络模型无法充分提取语义特征的问题,文中提出了基于BERT(bidirectional encoder representations from transformers)和双向...针对传统情感分析任务中,使用Word2Vec(word to vector)模型生成文本词向量无法有效解决多义词表征,经典神经网络模型无法充分提取语义特征的问题,文中提出了基于BERT(bidirectional encoder representations from transformers)和双向门控循环单元(bidirectional gated recurrent unit,BGRU)以及注意力机制(attention mechanism,Att)的BERT-BGRU-Att中文文本情感分析模型。首先通过BERT预训练模型将中文文本转变为矩阵向量的表示形式,然后建立融合注意力机制的BGRU神经网络对文本信息进行特征提取,最后将信息特征按照不同的权重输入到Softmax分类器进行预测。在网络评论数据集上进行实验,结果显示该模型的预测效果优于相关网络模型,表明该模型在中文文本情感分析任务中的有效性。展开更多
针对传统的命名实体识别方法无法充分学习古汉语复杂的句子结构信息以及在长序列特征提取过程中容易带来信息损失的问题,提出一种融合SikuBERT(Siku Bidirectional Encoder Representation from Transformers)模型与MHA(Multi-Head Atte...针对传统的命名实体识别方法无法充分学习古汉语复杂的句子结构信息以及在长序列特征提取过程中容易带来信息损失的问题,提出一种融合SikuBERT(Siku Bidirectional Encoder Representation from Transformers)模型与MHA(Multi-Head Attention)的古汉语命名实体识别方法。首先,利用SikuBERT模型对古汉语语料进行预训练,将训练得到的信息向量输入BiLSTM(Bidirectional Long Short-Term Memory)网络中提取特征,再将BiLSTM层的输出特征通过MHA分配不同的权重减少长序列的信息损失,最后通过CRF(Conditional Random Field)解码得到预测的序列标签。实验表明,与常用的BiLSTM-CRF、 BERT-BiLSTM-CRF等模型相比,该方法的F_(1)值有显著提升,证明了该方法能有效提升古汉语命名实体识别的效果。展开更多
In the era of big data,E-commerce plays an increasingly important role,and steel E-commerce certainly occupies a positive position.However,it is very difficult to choose satisfactory steel raw materials from diverse s...In the era of big data,E-commerce plays an increasingly important role,and steel E-commerce certainly occupies a positive position.However,it is very difficult to choose satisfactory steel raw materials from diverse steel commodities online on steel E-commerce platforms in the purchase of staffs.In order to improve the efficiency of purchasers searching for commodities on the steel E-commerce platforms,we propose a novel deep learning-based loss function for named entity recognition(NER).Considering the impacts of small sample and imbalanced data,in our NER scheme,the focal loss,the label smoothing,and the cross entropy are incorporated into a lite bidirectional encoder representations from transformers(BERT)model to avoid the over-fitting.Moreover,through the analysis of different classic annotation techniques used to tag data,an ideal one is chosen for the training model in our proposed scheme.Experiments are conducted on Chinese steel E-commerce datasets.The experimental results show that the training time of a lite BERT(ALBERT)-based method is much shorter than that of BERT-based models,while achieving the similar computational performance in terms of metrics precision,recall,and F1 with BERT-based models.Meanwhile,our proposed approach performs much better than that of combining Word2Vec,bidirectional long short-term memory(Bi-LSTM),and conditional random field(CRF)models,in consideration of training time and F1.展开更多
Offensive messages on social media,have recently been frequently used to harass and criticize people.In recent studies,many promising algorithms have been developed to identify offensive texts.Most algorithms analyze ...Offensive messages on social media,have recently been frequently used to harass and criticize people.In recent studies,many promising algorithms have been developed to identify offensive texts.Most algorithms analyze text in a unidirectional manner,where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences.In addition,there are many separate models for identifying offensive texts based on monolin-gual and multilingual,but there are a few models that can detect both monolingual and multilingual-based offensive texts.In this study,a detection system has been developed for both monolingual and multilingual offensive texts by combining deep convolutional neural network and bidirectional encoder representations from transformers(Deep-BERT)to identify offensive posts on social media that are used to harass others.This paper explores a variety of ways to deal with multilin-gualism,including collaborative multilingual and translation-based approaches.Then,the Deep-BERT is tested on the Bengali and English datasets,including the different bidirectional encoder representations from transformers(BERT)pre-trained word-embedding techniques,and found that the proposed Deep-BERT’s efficacy outperformed all existing offensive text classification algorithms reaching an accuracy of 91.83%.The proposed model is a state-of-the-art model that can classify both monolingual-based and multilingual-based offensive texts.展开更多
For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural net...For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.展开更多
文摘中文电子病历命名实体识别主要是研究电子病历病程记录文书数据集,文章提出对医疗手术麻醉文书数据集进行命名实体识别的研究。利用轻量级来自Transformer的双向编码器表示(A Lite Bidirectional Encoder Representation from Transformers,ALBERT)预训练模型微调数据集和Tranfomers中的trainer训练器训练模型的方法,实现在医疗手术麻醉文书上识别手术麻醉事件命名实体与获取复杂麻醉医疗质量控制指标值。文章为医疗手术麻醉文书命名实体识别提供了可借鉴的思路,并且为计算复杂麻醉医疗质量控制指标值提供了一种新的解决方案。
基金This research was funded by National Natural Science Foundation of China under Grant No.61806171Sichuan University of Science&Engineering Talent Project under Grant No.2021RC15+2 种基金Open Fund Project of Key Laboratory for Non-Destructive Testing and Engineering Computer of Sichuan Province Universities on Bridge Inspection and Engineering under Grant No.2022QYJ06Sichuan University of Science&Engineering Graduate Student Innovation Fund under Grant No.Y2023115The Scientific Research and Innovation Team Program of Sichuan University of Science and Technology under Grant No.SUSE652A006.
文摘While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.
基金The National Key R&D Program of China supported this study(2017YFC1700303).
文摘Background:The medical records of traditional Chinese medicine(TCM)contain numerous synonymous terms with different descriptions,which is not conducive to computer-aided data mining of TCM.However,there is a lack of models available to normalize synonymous TCM terms.Therefore,construction of a synonymous term conversion(STC)model for normalizing synonymous TCM terms is necessary.Methods:Based on the neural networks of bidirectional encoder representations from transformers(BERT),four types of TCM STC models were designed:Models based on BERT and text classification,text sequence generation,named entity recognition,and text matching.The superior STC model was selected on the basis of its performance in converting synonymous terms.Moreover,three misjudgment inspection methods for the conversion results of the STC model based on inconsistency were proposed to find incorrect term conversion:Neuron random deactivation,output comparison of multiple isomorphic models,and output comparison of multiple heterogeneous models(OCMH).Results:The classification-based STC model outperformed the other STC task models.It achieved F1 scores of 0.91,0.91,and 0.83 for performing symptoms,patterns,and treatments STC tasks,respectively.The OCMH method showed the best performance in misjudgment inspection,with wrong detection rates of 0.80,0.84,and 0.90 in the term conversion results for symptoms,patterns,and treatments,respectively.Conclusion:The TCM STC model based on classification achieved superior performance in converting synonymous terms for symptoms,patterns,and treatments.The misjudgment inspection method based on OCMH showed superior performance in identifying incorrect outputs.
文摘Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally intelligentmachines.Graph-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC tasks.However,their limited ability to collect and acquire contextual information hinders their effectiveness.We propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address this.The proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human emotions.Themodel used text augmentation techniques to producemore training data,improving the proposed model’s accuracy.Transformer encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual information.This integration improves the accuracy and robustness of the proposed model.Furthermore,we present a method for balancing the training dataset by creating enhanced samples from the original dataset.By balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed model.Experimental results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ERC.TA-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based encoding.The balanced dataset and the additional training samples also enhance its resilience.These findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.
文摘The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Current approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent summaries.This study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical information.Our approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text structures.This model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and Multi-News.The results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document contexts.This research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.
基金funded by Scientific Research Deanship at University of Ha’il-Saudi Arabia through Project Number RG-23092。
文摘Cyberbullying,a critical concern for digital safety,necessitates effective linguistic analysis tools that can navigate the complexities of language use in online spaces.To tackle this challenge,our study introduces a new approach employing Bidirectional Encoder Representations from the Transformers(BERT)base model(cased),originally pretrained in English.This model is uniquely adapted to recognize the intricate nuances of Arabic online communication,a key aspect often overlooked in conventional cyberbullying detection methods.Our model is an end-to-end solution that has been fine-tuned on a diverse dataset of Arabic social media(SM)tweets showing a notable increase in detection accuracy and sensitivity compared to existing methods.Experimental results on a diverse Arabic dataset collected from the‘X platform’demonstrate a notable increase in detection accuracy and sensitivity compared to existing methods.E-BERT shows a substantial improvement in performance,evidenced by an accuracy of 98.45%,precision of 99.17%,recall of 99.10%,and an F1 score of 99.14%.The proposed E-BERT not only addresses a critical gap in cyberbullying detection in Arabic online forums but also sets a precedent for applying cross-lingual pretrained models in regional language applications,offering a scalable and effective framework for enhancing online safety across Arabic-speaking communities.
文摘Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.
文摘针对传统的命名实体识别方法无法充分学习古汉语复杂的句子结构信息以及在长序列特征提取过程中容易带来信息损失的问题,提出一种融合SikuBERT(Siku Bidirectional Encoder Representation from Transformers)模型与MHA(Multi-Head Attention)的古汉语命名实体识别方法。首先,利用SikuBERT模型对古汉语语料进行预训练,将训练得到的信息向量输入BiLSTM(Bidirectional Long Short-Term Memory)网络中提取特征,再将BiLSTM层的输出特征通过MHA分配不同的权重减少长序列的信息损失,最后通过CRF(Conditional Random Field)解码得到预测的序列标签。实验表明,与常用的BiLSTM-CRF、 BERT-BiLSTM-CRF等模型相比,该方法的F_(1)值有显著提升,证明了该方法能有效提升古汉语命名实体识别的效果。
基金This work was supported in part by the National Natural Science Foundation of China under Grants U1836106 and 81961138010in part by the Beijing Natural Science Foundation under Grants M21032 and 19L2029+2 种基金in part by the Beijing Intelligent Logistics System Collaborative Innovation Center under Grant BILSCIC-2019KF-08in part by the Scientific and Technological Innovation Foundation of Shunde Graduate School,USTB,under Grants BK20BF010 and BK19BF006in part by the Fundamental Research Funds for the University of Science and Technology Beijing under Grant FRF-BD-19-012A.
文摘In the era of big data,E-commerce plays an increasingly important role,and steel E-commerce certainly occupies a positive position.However,it is very difficult to choose satisfactory steel raw materials from diverse steel commodities online on steel E-commerce platforms in the purchase of staffs.In order to improve the efficiency of purchasers searching for commodities on the steel E-commerce platforms,we propose a novel deep learning-based loss function for named entity recognition(NER).Considering the impacts of small sample and imbalanced data,in our NER scheme,the focal loss,the label smoothing,and the cross entropy are incorporated into a lite bidirectional encoder representations from transformers(BERT)model to avoid the over-fitting.Moreover,through the analysis of different classic annotation techniques used to tag data,an ideal one is chosen for the training model in our proposed scheme.Experiments are conducted on Chinese steel E-commerce datasets.The experimental results show that the training time of a lite BERT(ALBERT)-based method is much shorter than that of BERT-based models,while achieving the similar computational performance in terms of metrics precision,recall,and F1 with BERT-based models.Meanwhile,our proposed approach performs much better than that of combining Word2Vec,bidirectional long short-term memory(Bi-LSTM),and conditional random field(CRF)models,in consideration of training time and F1.
文摘Offensive messages on social media,have recently been frequently used to harass and criticize people.In recent studies,many promising algorithms have been developed to identify offensive texts.Most algorithms analyze text in a unidirectional manner,where a bidirectional method can maximize performance results and capture semantic and contextual information in sentences.In addition,there are many separate models for identifying offensive texts based on monolin-gual and multilingual,but there are a few models that can detect both monolingual and multilingual-based offensive texts.In this study,a detection system has been developed for both monolingual and multilingual offensive texts by combining deep convolutional neural network and bidirectional encoder representations from transformers(Deep-BERT)to identify offensive posts on social media that are used to harass others.This paper explores a variety of ways to deal with multilin-gualism,including collaborative multilingual and translation-based approaches.Then,the Deep-BERT is tested on the Bengali and English datasets,including the different bidirectional encoder representations from transformers(BERT)pre-trained word-embedding techniques,and found that the proposed Deep-BERT’s efficacy outperformed all existing offensive text classification algorithms reaching an accuracy of 91.83%.The proposed model is a state-of-the-art model that can classify both monolingual-based and multilingual-based offensive texts.
基金Supported by the National Key Research and Development Program of China(No.2018YFB1702601).
文摘For the existing aspect category sentiment analysis research,most of the aspects are given for sentiment extraction,and this pipeline method is prone to error accumulation,and the use of graph convolutional neural network for aspect category sentiment analysis does not fully utilize the dependency type information between words,so it cannot enhance feature extraction.This paper proposes an end-to-end aspect category sentiment analysis(ETESA)model based on type graph convolutional networks.The model uses the bidirectional encoder representation from transformers(BERT)pretraining model to obtain aspect categories and word vectors containing contextual dynamic semantic information,which can solve the problem of polysemy;when using graph convolutional network(GCN)for feature extraction,the fusion operation of word vectors and initialization tensor of dependency types can obtain the importance values of different dependency types and enhance the text feature representation;by transforming aspect category and sentiment pair extraction into multiple single-label classification problems,aspect category and sentiment can be extracted simultaneously in an end-to-end way and solve the problem of error accumulation.Experiments are tested on three public datasets,and the results show that the ETESA model can achieve higher Precision,Recall and F1 value,proving the effectiveness of the model.