随着能源行业的快速发展和技术革新,大量的专业术语和表达方式不断更新,新词不断涌现。然而,传统的新词发现方法通常依赖于词典或规则,且难以高效率地处理和更新大量的专业术语,特别是在快速变化的能源领域。因此,结合能源领域文本数据...随着能源行业的快速发展和技术革新,大量的专业术语和表达方式不断更新,新词不断涌现。然而,传统的新词发现方法通常依赖于词典或规则,且难以高效率地处理和更新大量的专业术语,特别是在快速变化的能源领域。因此,结合能源领域文本数据特性,提出了一种融合N-Gram和多重注意力机制的能源领域新词发现方法(new word discovery method in the energy field combining N-Gram and multiple attention mechanism, ENFM)。该方法首先利用N-Gram模型对能源领域的文本数据进行初步处理,通过统计和分析词频来生成新词候选列表。随后,引入融合多重注意力机制的ERNIE-BiLSTM-CRF模型,以进一步提升新词发现的准确性和效率。与传统的新词发现技术相比,在新词的准确识别和整体效率上均有显著提升,将其于能源领域政策文本数据集,准确率、召回率和F1分别为95.71%、95.56%、95.63%。实验结果表明,该方法能够准确地在能源领域的大量文本数据中识别新词,有效识别出能源领域特有的词汇和表达方式,显著提高了中文分词任务中对能源领域专业术语的识别能力。展开更多
The rapid growth of social networks has produced an unprecedented amount of user-generated data, which provides an excellent opportunity for text mining. Authorship analysis, an important part of text mining, attempts...The rapid growth of social networks has produced an unprecedented amount of user-generated data, which provides an excellent opportunity for text mining. Authorship analysis, an important part of text mining, attempts to learn about the author of the text through subtle variations in the writing styles that occur between gender, age and social groups. Such information has a variety of applications including advertising and law enforcement. One of the most accessible sources of user-generated data is Twitter, which makes the majority of its user data freely available through its data access API. In this study we seek to identify the gender of users on Twitter using Perceptron and Nai ve Bayes with selected 1 through 5-gram features from tweet text. Stream applications of these algorithms were employed for gender prediction to handle the speed and volume of tweet traffic. Because informal text, such as tweets, cannot be easily evaluated using traditional dictionary methods, n-gram features were implemented in this study to represent streaming tweets. The large number of 1 through 5-grams requires that only a subset of them be used in gender classification, for this reason informative n-gram features were chosen using multiple selection algorithms. In the best case the Naive Bayes and Perceptron algorithms produced accuracy, balanced accuracy, and F-measure above 99%.展开更多
A switch from avian-typeα-2,3 to human-typeα-2,6 receptors is an essential element for the initiation of a pandemic from an avian influenza virus.Some H9N2 viruses exhibit a preference for binding to human-typeα-2,...A switch from avian-typeα-2,3 to human-typeα-2,6 receptors is an essential element for the initiation of a pandemic from an avian influenza virus.Some H9N2 viruses exhibit a preference for binding to human-typeα-2,6 receptors.This identifies their potential threat to public health.However,our understanding of the molecular basis for the switch of receptor preference is still limited.In this study,we employed the random forest algorithm to identify the potentially key amino acid sites within hemagglutinin(HA),which are associated with the receptor binding ability of H9N2 avian influenza virus(AIV).Subsequently,these sites were further verified by receptor binding assays.A total of 12 substitutions in the HA protein(N158D,N158S,A160 N,A160D,A160T,T163I,T163V,V190T,V190A,D193 N,D193G,and N231D)were predicted to prefer binding toα-2,6 receptors.Except for the V190T substitution,the other substitutions were demonstrated to display an affinity for preferential binding toα-2,6 receptors by receptor binding assays.Especially,the A160T substitution caused a significant upregulation of immune-response genes and an increased mortality rate in mice.Our findings provide novel insights into understanding the genetic basis of receptor preference of the H9N2 AIV.展开更多
文摘随着能源行业的快速发展和技术革新,大量的专业术语和表达方式不断更新,新词不断涌现。然而,传统的新词发现方法通常依赖于词典或规则,且难以高效率地处理和更新大量的专业术语,特别是在快速变化的能源领域。因此,结合能源领域文本数据特性,提出了一种融合N-Gram和多重注意力机制的能源领域新词发现方法(new word discovery method in the energy field combining N-Gram and multiple attention mechanism, ENFM)。该方法首先利用N-Gram模型对能源领域的文本数据进行初步处理,通过统计和分析词频来生成新词候选列表。随后,引入融合多重注意力机制的ERNIE-BiLSTM-CRF模型,以进一步提升新词发现的准确性和效率。与传统的新词发现技术相比,在新词的准确识别和整体效率上均有显著提升,将其于能源领域政策文本数据集,准确率、召回率和F1分别为95.71%、95.56%、95.63%。实验结果表明,该方法能够准确地在能源领域的大量文本数据中识别新词,有效识别出能源领域特有的词汇和表达方式,显著提高了中文分词任务中对能源领域专业术语的识别能力。
文摘The rapid growth of social networks has produced an unprecedented amount of user-generated data, which provides an excellent opportunity for text mining. Authorship analysis, an important part of text mining, attempts to learn about the author of the text through subtle variations in the writing styles that occur between gender, age and social groups. Such information has a variety of applications including advertising and law enforcement. One of the most accessible sources of user-generated data is Twitter, which makes the majority of its user data freely available through its data access API. In this study we seek to identify the gender of users on Twitter using Perceptron and Nai ve Bayes with selected 1 through 5-gram features from tweet text. Stream applications of these algorithms were employed for gender prediction to handle the speed and volume of tweet traffic. Because informal text, such as tweets, cannot be easily evaluated using traditional dictionary methods, n-gram features were implemented in this study to represent streaming tweets. The large number of 1 through 5-grams requires that only a subset of them be used in gender classification, for this reason informative n-gram features were chosen using multiple selection algorithms. In the best case the Naive Bayes and Perceptron algorithms produced accuracy, balanced accuracy, and F-measure above 99%.
基金supported by the National Natural Science Foundation of China(32273037 and 32102636)the Guangdong Major Project of Basic and Applied Basic Research(2020B0301030007)+4 种基金Laboratory of Lingnan Modern Agriculture Project(NT2021007)the Guangdong Science and Technology Innovation Leading Talent Program(2019TX05N098)the 111 Center(D20008)the double first-class discipline promotion project(2023B10564003)the Department of Education of Guangdong Province(2019KZDXM004 and 2019KCXTD001).
文摘A switch from avian-typeα-2,3 to human-typeα-2,6 receptors is an essential element for the initiation of a pandemic from an avian influenza virus.Some H9N2 viruses exhibit a preference for binding to human-typeα-2,6 receptors.This identifies their potential threat to public health.However,our understanding of the molecular basis for the switch of receptor preference is still limited.In this study,we employed the random forest algorithm to identify the potentially key amino acid sites within hemagglutinin(HA),which are associated with the receptor binding ability of H9N2 avian influenza virus(AIV).Subsequently,these sites were further verified by receptor binding assays.A total of 12 substitutions in the HA protein(N158D,N158S,A160 N,A160D,A160T,T163I,T163V,V190T,V190A,D193 N,D193G,and N231D)were predicted to prefer binding toα-2,6 receptors.Except for the V190T substitution,the other substitutions were demonstrated to display an affinity for preferential binding toα-2,6 receptors by receptor binding assays.Especially,the A160T substitution caused a significant upregulation of immune-response genes and an increased mortality rate in mice.Our findings provide novel insights into understanding the genetic basis of receptor preference of the H9N2 AIV.
