With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service respons...With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service response provision.Knowledge graphs are usually constructed based on entity recognition.Specifically,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge fusion.In this work,the entities and characteristics of power entity recognition are analyzed,the mechanism of entity recognition is clarified,and entity recognition techniques are analyzed in the context of the power domain.Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated,and the two methods are comparatively analyzed.The results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the BLSTM.The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.展开更多
Named entity recognition is a fundamental task in biomedical data mining. In this letter, a named entity recognition system based on CRFs （Conditional Random Fields） for biomedical texts is presented. The system mak...Named entity recognition is a fundamental task in biomedical data mining. In this letter, a named entity recognition system based on CRFs （Conditional Random Fields） for biomedical texts is presented. The system makes extensive use of a diverse set of features, including local features, full text features and external resource features. All features incorporated in this system are described in detail, and the impacts of different feature sets on the performance of the system are evaluated. In order to improve the performance of system, post-processing modules are exploited to deal with the abbreviation phenomena, cascaded named entity and boundary errors identification. Evaluation on this system proved that the feature selection has important impact on the system performance, and the post-processing explored has an important contribution on system performance to achieve better resuits.展开更多
Chinese organization name recognition is hard and important in natural language processing. To reduce tagged corpus and use untagged corpus,we presented combing Co-training with support vector machines (SVM) and condi...Chinese organization name recognition is hard and important in natural language processing. To reduce tagged corpus and use untagged corpus,we presented combing Co-training with support vector machines (SVM) and conditional random fields (CRF) to improve recognition results. Based on principles of uncorrelated and compatible,we constructed different classifiers from different views within SVM or CRF alone and combination of these two models. And we modified a heuristic untagged samples selection algorithm to reduce time complexity. Experimental results show that under the same tagged data,Co-training has 10% F-measure higher than using SVM or CRF alone; under the same F-measure,Co-training saves at most 70% of tagged data to achieve the same performance.展开更多
针对命名实体识别(NER)任务中相关模型通常仅对字符及相关词汇进行建模,未充分利用汉字特有的字形结构信息和实体类型信息的问题,提出一种融合先验知识和字形特征的命名实体识别模型。首先,采用结合高斯注意力机制的Transformer对输入...针对命名实体识别(NER)任务中相关模型通常仅对字符及相关词汇进行建模,未充分利用汉字特有的字形结构信息和实体类型信息的问题,提出一种融合先验知识和字形特征的命名实体识别模型。首先,采用结合高斯注意力机制的Transformer对输入序列进行编码,并从中文维基百科中获取实体类型的中文释义,采用双向门控循环单元(BiGRU)编码实体类型信息作为先验知识,利用注意力机制将它与字符表示进行组合;其次,采用双向长短时记忆(BiLSTM)网络编码输入序列的远距离依赖关系,通过字形编码表获得繁体的仓颉码和简体的现代五笔码,采用卷积神经网络(CNN)提取字形特征表示,并根据不同权重组合繁体与简体字形特征,利用门控机制将它与经过BiLSTM编码后的字符表示进行组合;最后,使用条件随机场(CRF)解码,得到命名实体标注序列。在偏口语化的数据集Weibo、小型数据集Boson和大型数据集PeopleDaily上的实验结果表明,与基线模型MECT(Multi-metadata Embedding based Cross-Transformer)相比,所提模型的F1值别提高了2.47、1.20和0.98个百分点,验证了模型的有效性。展开更多
命名实体识别是知识抽取中的重要任务之一,为了更有效地利用词典匹配信息,提出了基于匹配词权重优化的中文命名实体识别模型。首先利用与训练模型和分词工具获得每个字符的向量表示和词性标注;然后在词典中匹配潜在词组,跟据匹配词词频...命名实体识别是知识抽取中的重要任务之一,为了更有效地利用词典匹配信息,提出了基于匹配词权重优化的中文命名实体识别模型。首先利用与训练模型和分词工具获得每个字符的向量表示和词性标注;然后在词典中匹配潜在词组,跟据匹配词词频和文档计数的优化权重对词组加权,结合字符向量得到字符的多特征融合表示;最后使用双向长短期记忆网络(Bi-directional Long-Short Term Memory,Bi-LSTM)网络进行训练,使用条件随机场(Conditional Random Field,CRF)完成标签推理得到识别实体。试验结果表明,该模型在Resume和影视-音乐-书籍数据集上的F1值分别达到了95.55%和85.39%,有效地提高了中文命名实体识别效果。展开更多
基金supported by Science and Technology Project of State Grid Corporation(Research and Application of Intelligent Energy Meter Quality Analysis and Evaluation Technology Based on Full Chain Data)
文摘With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service response provision.Knowledge graphs are usually constructed based on entity recognition.Specifically,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge fusion.In this work,the entities and characteristics of power entity recognition are analyzed,the mechanism of entity recognition is clarified,and entity recognition techniques are analyzed in the context of the power domain.Power entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated,and the two methods are comparatively analyzed.The results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the BLSTM.The CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.
基金Supported by The National Natural Science Foundation of China(No.60302021).
文摘Named entity recognition is a fundamental task in biomedical data mining. In this letter, a named entity recognition system based on CRFs （Conditional Random Fields） for biomedical texts is presented. The system makes extensive use of a diverse set of features, including local features, full text features and external resource features. All features incorporated in this system are described in detail, and the impacts of different feature sets on the performance of the system are evaluated. In order to improve the performance of system, post-processing modules are exploited to deal with the abbreviation phenomena, cascaded named entity and boundary errors identification. Evaluation on this system proved that the feature selection has important impact on the system performance, and the post-processing explored has an important contribution on system performance to achieve better resuits.
基金National Natural Science Foundations of China (No.60873179, No.60803078)
文摘Chinese organization name recognition is hard and important in natural language processing. To reduce tagged corpus and use untagged corpus,we presented combing Co-training with support vector machines (SVM) and conditional random fields (CRF) to improve recognition results. Based on principles of uncorrelated and compatible,we constructed different classifiers from different views within SVM or CRF alone and combination of these two models. And we modified a heuristic untagged samples selection algorithm to reduce time complexity. Experimental results show that under the same tagged data,Co-training has 10% F-measure higher than using SVM or CRF alone; under the same F-measure,Co-training saves at most 70% of tagged data to achieve the same performance.
文摘针对命名实体识别(NER)任务中相关模型通常仅对字符及相关词汇进行建模,未充分利用汉字特有的字形结构信息和实体类型信息的问题,提出一种融合先验知识和字形特征的命名实体识别模型。首先,采用结合高斯注意力机制的Transformer对输入序列进行编码,并从中文维基百科中获取实体类型的中文释义,采用双向门控循环单元(BiGRU)编码实体类型信息作为先验知识,利用注意力机制将它与字符表示进行组合;其次,采用双向长短时记忆(BiLSTM)网络编码输入序列的远距离依赖关系,通过字形编码表获得繁体的仓颉码和简体的现代五笔码,采用卷积神经网络(CNN)提取字形特征表示,并根据不同权重组合繁体与简体字形特征,利用门控机制将它与经过BiLSTM编码后的字符表示进行组合;最后,使用条件随机场(CRF)解码,得到命名实体标注序列。在偏口语化的数据集Weibo、小型数据集Boson和大型数据集PeopleDaily上的实验结果表明,与基线模型MECT(Multi-metadata Embedding based Cross-Transformer)相比,所提模型的F1值别提高了2.47、1.20和0.98个百分点,验证了模型的有效性。
文摘命名实体识别是知识抽取中的重要任务之一,为了更有效地利用词典匹配信息,提出了基于匹配词权重优化的中文命名实体识别模型。首先利用与训练模型和分词工具获得每个字符的向量表示和词性标注;然后在词典中匹配潜在词组,跟据匹配词词频和文档计数的优化权重对词组加权,结合字符向量得到字符的多特征融合表示;最后使用双向长短期记忆网络(Bi-directional Long-Short Term Memory,Bi-LSTM)网络进行训练,使用条件随机场(Conditional Random Field,CRF)完成标签推理得到识别实体。试验结果表明,该模型在Resume和影视-音乐-书籍数据集上的F1值分别达到了95.55%和85.39%,有效地提高了中文命名实体识别效果。
