论文投稿系统评审专家自动推荐模型研究
发布时间：2018-11-18 06:50
【摘要】： 文本自动分类是指在给定的分类体系下,根据文本内容自动确定文本所属类别。文本分类技术的出现,使文档可以自动地按照类别组织和处理,符合人类组织和处理信息的方式。同时,作为信息过滤、信息检索、搜索引擎等领域的技术基础,文本分类技术有着广泛的应用前景。 学报和学术会议所使用的论文投稿系统,涉及上千篇投稿论文要分配给上百位评审专家去审阅,在很短的时间内人工分配这些投稿论文给相关学科领域的专家们去评审往往匹配的不好。特别是评审专家的研究领域不清楚,人工无法及时、准确的收集到评审专家所属的学科领域信息,影响到论文分配任务的正常进行。选择合适的评审专家是正确评价投稿论文质量和提升学报、期刊学术层次的关键,如何用计算机来实现自动分配投稿论文给匹配领域的评审专家去审阅?文本自动分类可以很好的解决这个问题。 论文针对上述问题,提出一种基于文本分类技术的评审专家自动推荐模型,通过文本分类技术对投稿论文和对评审专家所发表的论文进行所属学科领域的分类,进而判断出评审专家的主要研究领域和投稿论文的学科领域。然后将投稿论文的学科领域与评审专家的研究领域自动匹配,建立自动推荐评审专家模型。论文的主要研究内容如下: ①在特征筛选中,引入最大频率的概念和特征项与类别的相关系数D ( m_(ik)),提出了改进的χ~2算法,实验结果表明,在特征项筛选中表现出了良好的筛选效果。 ②针对评审专家自动推荐模型选取的特征项为论文的关键词,在文本向量表示方法的基础上作了简化,提出了基于TF/IDF特征权重阈值的向量空间模型算法,并选用SVM分类方法对特征矩阵分类。实验结果表明,该算法可以有效的滤除不相关的噪声特征,产生更为准确的分类模型。 ③针对主动学习SVM分类算法在多类别的分类问题上存在分类器的速度随数目增加而变慢的问题,引入有向无环图SVM,改进了主动学习SVM分类算法,实验结果表明,改进后主动学习SVM分类算法可以增加交互的过程使训练得到的分类器具备自学习的能力,改进后主动学习SVM分类器在多类别的分类上能够精确分类并且提高分类速度。
[Abstract]:Automatic text categorization refers to the automatic classification of text according to the text content under a given classification system. The emergence of text categorization technology enables documents to be organized and processed automatically according to categories, in line with the way human beings organize and process information. At the same time, as the technical foundation of information filtering, information retrieval, search engine and other fields, text classification technology has a wide application prospect. The paper submission system used in journals and academic conferences involves the assignment of thousands of papers to hundreds of experts to review and review them. The manual assignment of these contributions to experts in related disciplines in a short period of time is often poorly matched. Especially, the research field of the review experts is not clear, and people can not collect the information of the subject fields of the review experts in time and accurately, which affects the normal work of the assignment of papers. It is the key to correctly evaluate the quality of contribution papers and improve the academic level of journals to select suitable review experts. How to use computer to automatically assign contribution papers to the evaluation experts in the matching field to review? Automatic text categorization can solve this problem very well. In order to solve the above problems, an automatic recommendation model of review experts based on text classification technology is put forward in this paper, which classifies the contribution papers and the papers published by the review experts through the text classification technology. The main research field and the subject field of the contribution paper are judged. Then the subject field of the contribution paper is automatically matched with the research field of the evaluation expert, and the expert model of automatic recommendation and evaluation is established. The main contents of this paper are as follows: 1 in feature selection, the concept of maximum frequency and the correlation coefficient D (m _ (ik),) are introduced. The experimental results show that, It shows good screening effect in feature selection. (2) aiming at the feature items selected from the automatic recommendation model of evaluation experts as the key words of the paper, the text vector representation method is simplified, and a vector space model algorithm based on TF/IDF feature weight threshold is proposed. SVM classification method is used to classify the feature matrix. Experimental results show that the algorithm can effectively filter irrelevant noise features and produce more accurate classification models. 3 in order to solve the problem that the speed of classifier becomes slower with the increase of the number of classifiers in the multi-class SVM classification algorithm, the active learning SVM classification algorithm is improved by introducing the directed acyclic graph SVM,. The improved active learning SVM classification algorithm can increase the interactive process so that the trained classifier has the ability of self-learning. The improved active learning SVM classifier can accurately classify and improve the classification speed in multi-category classification.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2009
【分类号】：TP182
本文编号：2339205
[Abstract]:Automatic text categorization refers to the automatic classification of text according to the text content under a given classification system. The emergence of text categorization technology enables documents to be organized and processed automatically according to categories, in line with the way human beings organize and process information. At the same time, as the technical foundation of information filtering, information retrieval, search engine and other fields, text classification technology has a wide application prospect. The paper submission system used in journals and academic conferences involves the assignment of thousands of papers to hundreds of experts to review and review them. The manual assignment of these contributions to experts in related disciplines in a short period of time is often poorly matched. Especially, the research field of the review experts is not clear, and people can not collect the information of the subject fields of the review experts in time and accurately, which affects the normal work of the assignment of papers. It is the key to correctly evaluate the quality of contribution papers and improve the academic level of journals to select suitable review experts. How to use computer to automatically assign contribution papers to the evaluation experts in the matching field to review? Automatic text categorization can solve this problem very well. In order to solve the above problems, an automatic recommendation model of review experts based on text classification technology is put forward in this paper, which classifies the contribution papers and the papers published by the review experts through the text classification technology. The main research field and the subject field of the contribution paper are judged. Then the subject field of the contribution paper is automatically matched with the research field of the evaluation expert, and the expert model of automatic recommendation and evaluation is established. The main contents of this paper are as follows: 1 in feature selection, the concept of maximum frequency and the correlation coefficient D (m _ (ik),) are introduced. The experimental results show that, It shows good screening effect in feature selection. (2) aiming at the feature items selected from the automatic recommendation model of evaluation experts as the key words of the paper, the text vector representation method is simplified, and a vector space model algorithm based on TF/IDF feature weight threshold is proposed. SVM classification method is used to classify the feature matrix. Experimental results show that the algorithm can effectively filter irrelevant noise features and produce more accurate classification models. 3 in order to solve the problem that the speed of classifier becomes slower with the increase of the number of classifiers in the multi-class SVM classification algorithm, the active learning SVM classification algorithm is improved by introducing the directed acyclic graph SVM,. The improved active learning SVM classification algorithm can increase the interactive process so that the trained classifier has the ability of self-learning. The improved active learning SVM classifier can accurately classify and improve the classification speed in multi-category classification.
【学位授予单位】：重庆大学
【学位级别】：硕士
【学位授予年份】：2009
【分类号】：TP182
【引证文献】
相关博士学位论文 前1条
1 向东;产品设计中多领域知识表达、获取及应用研究[D];华中科技大学;2012年
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