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提出了一种基于机器学习的Web文本自动分类的架构,提出了中文Web文档自动分类的主要技术问题。介绍了中文Web文档自动分类工具的总体设计,它主要包括网络蜘蛛、中文分词、特征选取和贝叶斯分类器等功能模块。最后对中文Web文档自动分类器进行了实验。 相似文献
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本文对文本分类过程中关键的部分进行了改进,在分词阶段,对分词的速度和精度进行了改进,在特征选取阶段,把多种特征选取方法进行了融合,最后对分类器进行了优化,并给出了实验测试的结果,实验的结果表明,文本分类的效率的确有了提高. 相似文献
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近年尽管针对中文本文分类的研究成果不少，但基于深度学习对中文政策等长文本进行自动分类的研究还不多见。为此，借鉴和拓展传统的数据增强方法，提出集成新时代人民日报分词语料库（NEPD）、简单数据增强（EDA）算法、word2vec和文本卷积神经网络（TextCNN）的NEWT新型计算框架；实证部分，基于中国地方政府发布的科技政策文本进行算法校验。实验结果显示，在取词长度分别为500、750和1 000词的情况下，应用NEWT算法对中文科技政策文本进行分类的效果优于RCNN、Bi-LSTM和CapsNet等传统深度学习模型，F1值的平均提升比例超过13%；同时，NEWT在较短取词长度下能够实现全文输入的近似效果，可以部分改善传统深度学习模型在中文长文本自动分类任务中的计算效率。 相似文献
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利用层次分析法(AHP)的原理和方法,探讨了中文文本分类系统影响因素的评价问题。首先,提出了影响文本分类系统性能的指标体系,建立了文本分类系统评价的层次结构模型;其次,根据专家调查的结果,构建比较判断矩阵;最后,利用AHP专用软件Expert Choice计算各层次评价指标的权重,并对结果进行了分析说明。 相似文献
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预处理是文本分类中的一个重要环节,预处理结果的好坏不仅关系到分类的准确度,而且关系到训练时间的长短和影响到分类的速度.采用一种基于词性选择的文本预处理方法进行文本预处理,该方法与传统方法进行了实验对比,结果显示,该方法降低了特征维数,同时保证了分类性能.实验表明该方法能够获得较好的分类效果. 相似文献
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自动文本分类技术研究 总被引：1，自引：0，他引：1
文本分类是数据挖掘的重要内容之一，在很多领域经常需要对文本信息进行处理、抽取、分类。通过分析了文本分类过程中涉及的文本表示、特征抽取、分类等方法，指出文本分类的基本特点。 相似文献
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文本分类是中文信息处理的热点研究内容,而语义是文本类别归属的依据。提出一种基于语义引导的特征选择方法,在特征选择的同时,对典型类别区分词进行加权,提高该类词在分类中的作用;采用支持向量机技术进行试验,实验表明建立语义知识库的特征选择改善了文本的分类性能。 相似文献
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基于词频的中文文本分类研究 总被引：1，自引：0，他引：1
本文对中文文本分类系统的设计和实现进行了阐述,对分类系统的系统结构、特征提取、训练算法、分类算法等进行了详细的介绍。将基于词频统计的方法应用于文本分类。并提出了一种基于汉语中单字词及二字词统计特性的中文文本分类方法,在无词表的情况下,通过统计构造单字和二字词表,对文本进行分类,并取得不错的效果。 相似文献
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《Information processing & management》2022,59(2):102798
Automated legal text classification is a prominent research topic in the legal field. It lays the foundation for building an intelligent legal system. Current literature focuses on international legal texts, such as Chinese cases, European cases, and Australian cases. Little attention is paid to text classification for U.S. legal texts. Deep learning has been applied to improving text classification performance. Its effectiveness needs further exploration in domains such as the legal field. This paper investigates legal text classification with a large collection of labeled U.S. case documents through comparing the effectiveness of different text classification techniques. We propose a machine learning algorithm using domain concepts as features and random forests as the classifier. Our experiment results on 30,000 full U.S. case documents in 50 categories demonstrated that our approach significantly outperforms a deep learning system built on multiple pre-trained word embeddings and deep neural networks. In addition, applying only the top 400 domain concepts as features for building the random forests could achieve the best performance. This study provides a reference to select machine learning techniques for building high-performance text classification systems in the legal domain or other fields. 相似文献
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Automatic text classification is the problem of automatically assigning predefined categories to free text documents, thus allowing for less manual labors required by traditional classification methods. When we apply binary classification to multi-class classification for text classification, we usually use the one-against-the-rest method. In this method, if a document belongs to a particular category, the document is regarded as a positive example of that category; otherwise, the document is regarded as a negative example. Finally, each category has a positive data set and a negative data set. But, this one-against-the-rest method has a problem. That is, the documents of a negative data set are not labeled manually, while those of a positive set are labeled by human. Therefore, the negative data set probably includes a lot of noisy data. In this paper, we propose that the sliding window technique and the revised EM (Expectation Maximization) algorithm are applied to binary text classification for solving this problem. As a result, we can improve binary text classification through extracting potentially noisy documents from the negative data set using the sliding window technique and removing actually noisy documents using the revised EM algorithm. The results of our experiments showed that our method achieved better performance than the original one-against-the-rest method in all the data sets and all the classifiers used in the experiments. 相似文献
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本文阐述了一种基于特征词聚类的降维方式,其主要思想就是把词在文本中的出现看成一个事件,先通过搜索算法计算每一个特征词的分布,合并对分类有相似作用的特征词,从而起到了特征降维的作用。最后通过实验测试分析,提出了一种改进的、考虑全局簇信息的相似度计算公式,将其应用到文本分类中,实验表明提高了文本分类的精度。 相似文献
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文本自动分类技术研究综述 总被引：2，自引：0，他引：2
文章从文本表示、特征选择、分类算法、常用基准语料以及评估指标等方面对近年来的研究成果进行综述并讨论。认为短文本分类和多语言文本分类管理是新出现的重要且紧迫的问题,并对这两个问题以及数据集偏斜、多层分类、标注瓶颈等几个关键问题进行重点讨论。最后总结并展望这些研究内容。 相似文献
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Automatic text classification is the task of organizing documents into pre-determined classes, generally using machine learning algorithms. Generally speaking, it is one of the most important methods to organize and make use of the gigantic amounts of information that exist in unstructured textual format. Text classification is a widely studied research area of language processing and text mining. In traditional text classification, a document is represented as a bag of words where the words in other words terms are cut from their finer context i.e. their location in a sentence or in a document. Only the broader context of document is used with some type of term frequency information in the vector space. Consequently, semantics of words that can be inferred from the finer context of its location in a sentence and its relations with neighboring words are usually ignored. However, meaning of words, semantic connections between words, documents and even classes are obviously important since methods that capture semantics generally reach better classification performances. Several surveys have been published to analyze diverse approaches for the traditional text classification methods. Most of these surveys cover application of different semantic term relatedness methods in text classification up to a certain degree. However, they do not specifically target semantic text classification algorithms and their advantages over the traditional text classification. In order to fill this gap, we undertake a comprehensive discussion of semantic text classification vs. traditional text classification. This survey explores the past and recent advancements in semantic text classification and attempts to organize existing approaches under five fundamental categories; domain knowledge-based approaches, corpus-based approaches, deep learning based approaches, word/character sequence enhanced approaches and linguistic enriched approaches. Furthermore, this survey highlights the advantages of semantic text classification algorithms over the traditional text classification algorithms. 相似文献
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Text clustering is a well-known method for information retrieval and numerous methods for classifying words, documents or both together have been proposed. Frequently, textual data are encoded using vector models so the corpus is transformed in to a matrix of terms by documents; using this representation text clustering generates groups of similar objects on the basis of the presence/absence of the words in the documents. An alternative way to work on texts is to represent them as a network where nodes are entities connected by the presence and distribution of the words in the documents. In this work, after summarising the state of the art of text clustering we will present a new network approach to textual data. We undertake text co-clustering using methods developed for social network analysis. Several experimental results will be presented to demonstrate the validity of the approach and the advantages of this technique compared to existing methods. 相似文献
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自然语言理解心理学在短文本分类中的实证研究 总被引：1，自引：0，他引：1
目前对文本分类研究多数集中在对大规模语料基础上的特征选择或分类器算法的研究。本文是建立在训练样本少且样本长度短的基础上,根据人脑对自然语言理解的心理学原理"人们总是根据已知的最熟悉的、最典型的例子进行判断,只有在该方法不奏效的时候才使用频率这一概念,并且使用的是十分简单的频率"从该角度进行短文本分类的实证研究。以心理学中的"熟悉原理"、"典型原理"等为模型建立特殊词库和典型案例词库,改进了传统文本分类的实验步骤,同时提出了该方法的优势和局限性。 相似文献