检索结果-南通市图书馆

short texts Classification Through Reference Document Expansion

Chinese Journal of Electronics 2014年第2期23卷 315-321页

作者： YANG Zhen FAN Kefeng LAI Yingxu GAO Kaiming WANG Yong College of Computer Science Beijing University of Technology China Electronics Standardization Institute CSIP Guangxi Section Guilin University of Electronic Technology

With the rapid development of information technology, short texts arising from socialized human interaction are gradually predominant in network information streams. Accelerating demands are requiring the industry to provide more effective classification of the brief ***, faced with short text documents, each of which contains only a few words, traditional document classification models run into difficulty. Aggressive documents expansion works remarkably well for many cases but suffers from the assumption of independent, identically distributed observations. We formalize a view of classification using Bayesian decision theory, treat each short text as observations from a probabilistic model, called a statistical language model, and encode classification preferences with a loss function defined by the language models and the external reference document. According to Vapnik’s methods of Structural risk minimization(SRM), the optimal classification action is the one that minimizes the structural risk, which provides a result that allows one to trade off errors on the training sample against improved generalization performance. We conduct experiments by using several corpora of microblog-like data, and analyze the experimental results. With respect to established baselines,results of these experiments show that applying our proposed document expansion method produces better chance to achieve the improved classification performance.

关键词： Text classification short texts Language model Document expansion External reference

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Sentiment Analysis of short texts Based on Parallel DenseNet

引用

Computers, Materials & Continua 2021年第10期69卷 51-65页

作者： Luqi Yan Jin Han Yishi Yue Liu Zhang Yannan Qian Nanjing University of Information Science&Technology Nanjing210044China State Grid Hunan Electric Power Company Limited Research Institute Changsha410007China Waterford Institute of Technology WaterfordX91 K0EKIreland

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 *** this paper,based on traditional densely connected convolutional networks(DenseNet),a parallel DenseNet is proposed to realize sentiment analysis of short ***,this paper proposes two novel feature extraction blocks that are based on DenseNet and a multiscale convolutional neural ***,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 ***,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.

关键词： Sentiment analysis short texts parallel DenseNet

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Enriching short text representation in microblog for clustering

引用

Frontiers of Computer Science 2012年第1期6卷 88-101页

作者： Jiliang TANG Xufei WANG Huiji GAO Xia HU Huan LIU Computer Science ＆ Engineering Arizona State University Tempe AZ 85281 USA

Social media websites allow users to exchange short texts such as tweets via microblogs and user status in friendship networks. Their limited length, pervasive abbrevi- ations, and coined acronyms and words exacerbate the prob- lems of synonymy and polysemy, and bring about new chal- lenges to data mining applications such as text clustering and classification. To address these issues, we dissect some poten- tial causes and devise an efficient approach that enriches data representation by employing machine translation to increase the number of features from different languages. Then we propose a novel framework which performs multi-language knowledge integration and feature reduction simultaneously through matrix factorization techniques. The proposed ap- proach is evaluated extensively in terms of effectiveness on two social media datasets from Facebook and Twitter. With its significant performance improvement, we further investi- gate potential factors that contribute to the improved perfor- mance.

关键词： short texts text representation multi-languageknowledge matrix factorization social media

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：