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
航行通告是民用航空情报领域的重要情报资料,针对中文航行通告专业名词较多、格式不统一及语义复杂等问题,提出了一种基于BERT-Bi-LSTM-CRF的实体识别模型,对航行通告E项内容中事件要素实体进行抽取。首先通过BERT(bidirectional encode...航行通告是民用航空情报领域的重要情报资料,针对中文航行通告专业名词较多、格式不统一及语义复杂等问题,提出了一种基于BERT-Bi-LSTM-CRF的实体识别模型,对航行通告E项内容中事件要素实体进行抽取。首先通过BERT(bidirectional encoder representations from transforms)模型对处理后的向量进行预训练,捕捉丰富的语义特征,然后传送至双向长短期记忆网络(bidirectional long short-term memory,Bi-LSTM)模型对上下文特征进行提取,最后利用条件随机场(conditional random field,CRF)模型对最佳实体标签预测并输出。收集并整理机场类航行通告相关的原始语料,经过文本标注与数据预处理,形成了可用于实体识别实验的训练集、验证集和评价集数据。基于此数据与不同的实体识别模型进行对比实验,BERT-Bi-LSTM-CRF模型的准确率为89.68%、召回率为81.77%、F_(1)为85.54%,其中F 1相比现有模型得到有效提升,结果验证了该模型在机场类航行通告中要素实体识别的有效性。展开更多
文本会话抽取将网络聊天记录等短文本信息流中的信息根据其所属的会话分检到多个会话队列,有利于短文本信息的管理及进一步的挖掘.现有的会话抽取技术主要对基于文本相似度的聚类方法进行改进,面临着短文本信息流的特征稀疏性、奇异性...文本会话抽取将网络聊天记录等短文本信息流中的信息根据其所属的会话分检到多个会话队列,有利于短文本信息的管理及进一步的挖掘.现有的会话抽取技术主要对基于文本相似度的聚类方法进行改进,面临着短文本信息流的特征稀疏性、奇异性和动态性等挑战.针对这些挑战,研究无监督的会话抽取技术,提出了一种基于信息流时序特征和上下文相关度的抽取方法.首先研究了信息流的会话生命周期规律,提出基于信息产生频率的会话边界检测方法;其次提出信息间的上下文相关度概念,采用基于实例的机器学习方法计算该相关度;最后综合信息产生频率和上下文相关度,设计了基于Single-Pass聚类模型的会话在线抽取算法SPFC(single-pass based on frequency and correlation).真实数据集上的实验结果表明,SPFC算法与已有的基于文本相似度的会话抽取算法相比,F1评测指标提高了30%.展开更多
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.
文摘航行通告是民用航空情报领域的重要情报资料,针对中文航行通告专业名词较多、格式不统一及语义复杂等问题,提出了一种基于BERT-Bi-LSTM-CRF的实体识别模型,对航行通告E项内容中事件要素实体进行抽取。首先通过BERT(bidirectional encoder representations from transforms)模型对处理后的向量进行预训练,捕捉丰富的语义特征,然后传送至双向长短期记忆网络(bidirectional long short-term memory,Bi-LSTM)模型对上下文特征进行提取,最后利用条件随机场(conditional random field,CRF)模型对最佳实体标签预测并输出。收集并整理机场类航行通告相关的原始语料,经过文本标注与数据预处理,形成了可用于实体识别实验的训练集、验证集和评价集数据。基于此数据与不同的实体识别模型进行对比实验,BERT-Bi-LSTM-CRF模型的准确率为89.68%、召回率为81.77%、F_(1)为85.54%,其中F 1相比现有模型得到有效提升,结果验证了该模型在机场类航行通告中要素实体识别的有效性。
文摘文本会话抽取将网络聊天记录等短文本信息流中的信息根据其所属的会话分检到多个会话队列,有利于短文本信息的管理及进一步的挖掘.现有的会话抽取技术主要对基于文本相似度的聚类方法进行改进,面临着短文本信息流的特征稀疏性、奇异性和动态性等挑战.针对这些挑战,研究无监督的会话抽取技术,提出了一种基于信息流时序特征和上下文相关度的抽取方法.首先研究了信息流的会话生命周期规律,提出基于信息产生频率的会话边界检测方法;其次提出信息间的上下文相关度概念,采用基于实例的机器学习方法计算该相关度;最后综合信息产生频率和上下文相关度,设计了基于Single-Pass聚类模型的会话在线抽取算法SPFC(single-pass based on frequency and correlation).真实数据集上的实验结果表明,SPFC算法与已有的基于文本相似度的会话抽取算法相比,F1评测指标提高了30%.
