Text sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks(CNNs).However,most of these CNN models focus only on learning local...Text sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks(CNNs).However,most of these CNN models focus only on learning local features while ignoring global features.In this paper,based on traditional densely connected convolutional networks(DenseNet),a parallel DenseNet is proposed to realize sentiment analysis of short texts.First,this paper proposes two novel feature extraction blocks that are based on DenseNet and a multiscale convolutional neural network.Second,this paper solves the problem of ignoring global features in traditional CNN models by combining the original features with features extracted by the parallel feature extraction block,and then sending the combined features into the final classifier.Last,a model based on parallel DenseNet that is capable of simultaneously learning both local and global features of short texts and shows better performance on six different databases compared to other basic models is proposed.展开更多
The present study is a contrastive study of inter-sentence conjunctions in Chinese/English legal parallel texts. Conjunction is one of the five cohesive devices put forward by Halliday and Hasan (1976). Many scholars ...The present study is a contrastive study of inter-sentence conjunctions in Chinese/English legal parallel texts. Conjunction is one of the five cohesive devices put forward by Halliday and Hasan (1976). Many scholars have applied their model of cohesion to the study of English and Chinese languages. As for the use of conjunction in Chinese and English, most scholars believe that there are more cases of conjunction in the English legal texts than in the Chinese ones because it is generally considered that Chinese is predominantly paratactic and English mainly hypotactic. Besides, up to now little detailed contrastive study has been done on conjunctions in Chinese/English non-literary texts.展开更多
The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parall...The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parallel naive Bayes algorithm(PNBA)for Chinese text classification based on Spark,a parallel memory computing platform for big data.This algorithm has implemented parallel operation throughout the entire training and prediction process of naive Bayes classifier mainly by adopting the programming model of resilient distributed datasets(RDD).For comparison,a PNBA based on Hadoop is also implemented.The test results show that in the same computing environment and for the same text sets,the Spark PNBA is obviously superior to the Hadoop PNBA in terms of key indicators such as speedup ratio and scalability.Therefore,Spark-based parallel algorithms can better meet the requirement of large-scale Chinese text data mining.展开更多
SimCSE框架仅使用分类令牌[CLS]token作为文本向量,同时忽略基座模型内层级信息,导致对基座模型输出语义特征提取不充分.本文基于SimCSE框架提出一种融合预训练模型层级特征方法SimCSE-HFF(SimCSE with hierarchical feature fusion,Sim...SimCSE框架仅使用分类令牌[CLS]token作为文本向量,同时忽略基座模型内层级信息,导致对基座模型输出语义特征提取不充分.本文基于SimCSE框架提出一种融合预训练模型层级特征方法SimCSE-HFF(SimCSE with hierarchical feature fusion,SimCSE-HFF).SimCSE-HFF基于双路并行网络,使用短路径和长路径强化特征学习,短路径使用卷积神经网络学习文本局部特征并进行降维,长路径使用双向门控循环神经网络学习深度语义信息,同时在长路径中利用自编码器融合基座模型内部其他层特征,解决模型对输出特征提取不充分的问题.在STS-B的中文与英文数据集上,SimCSE-HFF方法效果在语义相似度Spearman和Pearson相关性指标上优于传统方法,在不同预训练模型上均得到提升;在下游任务检索问答上也优于SimCSE框架,具有更优秀的通用性.展开更多
Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature ...Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.展开更多
This study investigates the feasibility of applying complex networks to fine-grained language classification and of employing word co-occurrence networks based on parallel texts as a substitute for syntactic dependenc...This study investigates the feasibility of applying complex networks to fine-grained language classification and of employing word co-occurrence networks based on parallel texts as a substitute for syntactic dependency networks in complex-network-based language classification.14 word co-occurrence networks were constructed based on parallel texts of 12 Slavic languages and 2 non-Slavic languages,respectively.With appropriate combinations of major parameters of these networks,cluster analysis was able to distinguish the Slavic languages from the non-Slavic and correctly group the Slavic languages into their respective sub-branches.Moreover,the clustering could also capture the genetic relationships of some of these Slavic languages within their sub-branches.The results have shown that word co-occurrence networks based on parallel texts are applicable to fine-grained language classification and they constitute a more convenient substitute for syntactic dependency networks in complex-network-based language classification.展开更多
基金This work was supported by the National Key R&D Program of China under Grant Number 2018YFB1003205by the National Natural Science Foundation of China under Grant Numbers U1836208,U1536206,U1836110,61602253,and 61672294+3 种基金by the Startup Foundation for Introducing Talent of NUIST(1441102001002)by the Jiangsu Basic Research Programs-Natural Science Foundation under Grant Number BK20181407by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundby the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘Text sentiment analysis is a common problem in the field of natural language processing that is often resolved by using convolutional neural networks(CNNs).However,most of these CNN models focus only on learning local features while ignoring global features.In this paper,based on traditional densely connected convolutional networks(DenseNet),a parallel DenseNet is proposed to realize sentiment analysis of short texts.First,this paper proposes two novel feature extraction blocks that are based on DenseNet and a multiscale convolutional neural network.Second,this paper solves the problem of ignoring global features in traditional CNN models by combining the original features with features extracted by the parallel feature extraction block,and then sending the combined features into the final classifier.Last,a model based on parallel DenseNet that is capable of simultaneously learning both local and global features of short texts and shows better performance on six different databases compared to other basic models is proposed.
文摘The present study is a contrastive study of inter-sentence conjunctions in Chinese/English legal parallel texts. Conjunction is one of the five cohesive devices put forward by Halliday and Hasan (1976). Many scholars have applied their model of cohesion to the study of English and Chinese languages. As for the use of conjunction in Chinese and English, most scholars believe that there are more cases of conjunction in the English legal texts than in the Chinese ones because it is generally considered that Chinese is predominantly paratactic and English mainly hypotactic. Besides, up to now little detailed contrastive study has been done on conjunctions in Chinese/English non-literary texts.
基金Project(KC18071)supported by the Application Foundation Research Program of Xuzhou,ChinaProjects(2017YFC0804401,2017YFC0804409)supported by the National Key R&D Program of China
文摘The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parallel naive Bayes algorithm(PNBA)for Chinese text classification based on Spark,a parallel memory computing platform for big data.This algorithm has implemented parallel operation throughout the entire training and prediction process of naive Bayes classifier mainly by adopting the programming model of resilient distributed datasets(RDD).For comparison,a PNBA based on Hadoop is also implemented.The test results show that in the same computing environment and for the same text sets,the Spark PNBA is obviously superior to the Hadoop PNBA in terms of key indicators such as speedup ratio and scalability.Therefore,Spark-based parallel algorithms can better meet the requirement of large-scale Chinese text data mining.
文摘SimCSE框架仅使用分类令牌[CLS]token作为文本向量,同时忽略基座模型内层级信息,导致对基座模型输出语义特征提取不充分.本文基于SimCSE框架提出一种融合预训练模型层级特征方法SimCSE-HFF(SimCSE with hierarchical feature fusion,SimCSE-HFF).SimCSE-HFF基于双路并行网络,使用短路径和长路径强化特征学习,短路径使用卷积神经网络学习文本局部特征并进行降维,长路径使用双向门控循环神经网络学习深度语义信息,同时在长路径中利用自编码器融合基座模型内部其他层特征,解决模型对输出特征提取不充分的问题.在STS-B的中文与英文数据集上,SimCSE-HFF方法效果在语义相似度Spearman和Pearson相关性指标上优于传统方法,在不同预训练模型上均得到提升;在下游任务检索问答上也优于SimCSE框架,具有更优秀的通用性.
基金supported by the Science and Technology Plan Projects of Sichuan Province of China under Grant No.2008GZ0003the Key Technologies R & D Program of Sichuan Province of China under Grant No.2008SZ0100
文摘Feature selection is one of the important topics in text classification. However, most of existing feature selection methods are serial and inefficient to be applied to massive text data sets. In this case, a feature selection method based on parallel collaborative evolutionary genetic algorithm is presented. The presented method uses genetic algorithm to select feature subsets and takes advantage of parallel collaborative evolution to enhance time efficiency, so it can quickly acquire the feature subsets which are more representative. The experimental results show that, for accuracy ratio and recall ratio, the presented method is better than information gain, x2 statistics, and mutual information methods; the consumed time of the presented method with only one CPU is inferior to that of these three methods, but the presented method is supe rior after using the parallel strategy.
基金supported by the National Social Science Foundation of China (09BYY024 and 11&ZD188)
文摘This study investigates the feasibility of applying complex networks to fine-grained language classification and of employing word co-occurrence networks based on parallel texts as a substitute for syntactic dependency networks in complex-network-based language classification.14 word co-occurrence networks were constructed based on parallel texts of 12 Slavic languages and 2 non-Slavic languages,respectively.With appropriate combinations of major parameters of these networks,cluster analysis was able to distinguish the Slavic languages from the non-Slavic and correctly group the Slavic languages into their respective sub-branches.Moreover,the clustering could also capture the genetic relationships of some of these Slavic languages within their sub-branches.The results have shown that word co-occurrence networks based on parallel texts are applicable to fine-grained language classification and they constitute a more convenient substitute for syntactic dependency networks in complex-network-based language classification.
