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We present a term weighting approach for improving web page classification, based on the assumption that the images of a web page are those elements which mainly attract the attention of the user. This assumption implies that the text contained in the visual block in which an image is located, called image-block, should contain significant information about the page contents. In this paper we propose a new metric, called the Inverse Term Importance Metric, aimed at assigning higher weights to important terms contained into important image-blocks identified by performing a visual layout analysis. We propose different methods to estimate the visual image-blocks importance, to smooth the term weight according to the importance of the blocks in which the term is located. The traditional TFxIDF model is modified accordingly and used in the classification task. The effectiveness of this new metric and the proposed block evaluation methods have been validated using different classification algorithms. 相似文献
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Chun-Yan Liang Li GuoZhao-Jie Xia Feng-Guang NieXiao-Xia Li Liang SuZhang-Yuan Yang 《Information processing & management》2006
A new dictionary-based text categorization approach is proposed to classify the chemical web pages efficiently. Using a chemistry dictionary, the approach can extract chemistry-related information more exactly from web pages. After automatic segmentation on the documents to find dictionary terms for document expansion, the approach adopts latent semantic indexing (LSI) to produce the final document vectors, and the relevant categories are finally assigned to the test document by using the k-NN text categorization algorithm. The effects of the characteristics of chemistry dictionary and test collection on the categorization efficiency are discussed in this paper, and a new voting method is also introduced to improve the categorization performance further based on the collection characteristics. The experimental results show that the proposed approach has the superior performance to the traditional categorization method and is applicable to the classification of chemical web pages. 相似文献
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文本自动分类是文本信息处理中的一项基础性工作。将范例推理应用于文本分类中,并利用词语间的词共现信息从文本中抽取主题词和频繁词共现项目集,以及借助聚类算法对范例库进行索引,实现了基于范例推理的文本自动分类系统。实验表明,与基于TFIDF的文本表示方法和最近邻分类算法相比,基于词共现信息的文本表示方法和范例库的聚类索引能有效地改善分类的准确性和效率,从而拓宽了范例推理的应用领域。 相似文献
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The goal of the study presented in this article is to investigate to what extent the classification of a web page by a single genre matches the users’ perspective. The extent of agreement on a single genre label for a web page can help understand whether there is a need for a different classification scheme that overrides the single-genre labelling. My hypothesis is that a single genre label does not account for the users’ perspective. In order to test this hypothesis, I submitted a restricted number of web pages (25 web pages) to a large number of web users (135 subjects) asking them to assign only a single genre label to each of the web pages. Users could choose from a list of 21 genre labels, or select one of the two ‘escape’ options, i.e. ‘Add a label’ and ‘I don’t know’. The rationale was to observe the level of agreement on a single genre label per web page, and draw some conclusions about the appropriateness of limiting the assignment to only a single label when doing genre classification of web pages. Results show that users largely disagree on the label to be assigned to a web page. 相似文献
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JavaScript传统上是单线程的,在HTML页面中执行一个需较长时间运行的脚本会阻塞所有的页面功能直至脚本完成。Web Worker是HTML5提供的JavaScript多线程解决方案。解析了Web Worker的工作原理和过程;提供了Web Worker代码示例和代码调试方法;说明了使用Web Worker如何提高Web应用的性能。由于Web Worker相对较新,目前关于Web Worker的示例和文献非常有限。该研究院提供了Web Worker的参考应用场景及进一步研究和应用的方向。 相似文献
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This paper examines several different approaches to exploiting structural information in semi-structured document categorization. The methods under consideration are designed for categorization of documents consisting of a collection of fields, or arbitrary tree-structured documents that can be adequately modeled with such a flat structure. The approaches range from trivial modifications of text modeling to more elaborate schemes, specifically tailored to structured documents. We combine these methods with three different text classification algorithms and evaluate their performance on four standard datasets containing different types of semi-structured documents. The best results were obtained with stacking, an approach in which predictions based on different structural components are combined by a meta classifier. A further improvement of this method is achieved by including the flat text model in the final prediction. 相似文献
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《Information processing & management》2023,60(3):103320
We propose a CNN-BiLSTM-Attention classifier to classify online short messages in Chinese posted by users on government web portals, so that a message can be directed to one or more government offices. Our model leverages every bit of information to carry out multi-label classification, to make use of different hierarchical text features and the labels information. In particular, our designed method extracts label meaning, the CNN layer extracts local semantic features of the texts, the BiLSTM layer fuses the contextual features of the texts and the local semantic features, and the attention layer selects the most relevant features for each label. We evaluate our model on two public large corpuses, and our high-quality handcraft e-government multi-label dataset, which is constructed by the text annotation tool doccano and consists of 29920 data points. Experimental results show that our proposed method is effective under common multi-label evaluation metrics, achieving micro-f1 of 77.22%, 84.42%, 87.52%, and marco-f1 of 77.68%, 73.37%, 83.57% on these three datasets respectively, confirming that our classifier is robust. We conduct ablation study to evaluate our label embedding method and attention mechanism. Moreover, case study on our handcraft e-government multi-label dataset verifies that our model integrates all types of semantic information of short messages based on different labels to achieve text classification. 相似文献
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在文本自动分类中,目前有词频和文档频率统计这两种概率估算方法,采用的估算方法恰当与否会直接影响特征抽取的质量与分类的准确度。本文采用K最近邻算法实现中文文本分类器,在中文平衡与非平衡两种训练语料下进行了训练与分类实验,实验数据表明使用非平衡语料语料时,可以采用基于词频的概率估算方法,使用平衡语料语料时,采用基于文档频率的概率估算方法,能够有效地提取高质量的文本特征,从而提高分类的准确度。 相似文献
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This paper presents a classifier for text data samples consisting of main text and additional components, such as Web pages and technical papers. We focus on multiclass and single-labeled text classification problems and design the classifier based on a hybrid composed of probabilistic generative and discriminative approaches. Our formulation considers individual component generative models and constructs the classifier by combining these trained models based on the maximum entropy principle. We use naive Bayes models as the component generative models for the main text and additional components such as titles, links, and authors, so that we can apply our formulation to document and Web page classification problems. Our experimental results for four test collections confirmed that our hybrid approach effectively combined main text and additional components and thus improved classification performance. 相似文献
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随着网络的飞速发展,网页数量急剧膨胀,近几年来更是以指数级进行增长,搜索引擎面临的挑战越来越严峻,很难从海量的网页中准确快捷地找到符合用户需求的网页。网页分类是解决这个问题的有效手段之一,基于网页主题分类和基于网页体裁分类是网页分类的两大主流,二者有效地提高了搜索引擎的检索效率。网页体裁分类是指按照网页的表现形式及其用途对网页进行分类。介绍了网页体裁的定义,网页体裁分类研究常用的分类特征,并且介绍了几种常用特征筛选方法、分类模型以及分类器的评估方法,为研究者提供了对网页体裁分类的概要性了解。 相似文献
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Broken hypertext links are a frequent problem in the Web. Sometimes the page which a link points to has disappeared forever, but in many other cases the page has simply been moved to another location in the same web site or to another one. In some cases the page besides being moved, is updated, becoming a bit different to the original one but rather similar. In all these cases it can be very useful to have a tool that provides us with pages highly related to the broken link, since we could select the most appropriate one. The relationship between the broken link and its possible linkable pages, can be defined as a function of many factors. In this work we have employed several resources both in the context of the link and in the Web to look for pages related to a broken link. From the resources in the context of a link, we have analyzed several sources of information such as the anchor text, the text surrounding the anchor, the URL and the page containing the link. We have also extracted information about a link from the Web infrastructure such as search engines, Internet archives and social tagging systems. We have combined all of these resources to design a system that recommends pages that can be used to recover the broken link. A novel methodology is presented to evaluate the system without resorting to user judgments, thus increasing the objectivity of the results, and helping to adjust the parameters of the algorithm. We have also compiled a web page collection with true broken links, which has been used to test the full system by humans. 相似文献
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Due to a large variety of noisy information embedded in Web pages, Web-page classification is much more difficult than pure-text classification. In this paper, we propose to improve the Web-page classification performance by removing the noise through summarization techniques. We first give empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web-page classification algorithms. We then put forward a new Web-page summarization algorithm based on Web-page layout and evaluate it along with several other state-of-the-art text summarization algorithms on the LookSmart Web directory. Experimental results show that the classification algorithms (NB or SVM) augmented by any summarization approach can achieve an improvement by more than 5.0% as compared to pure-text-based classification algorithms. We further introduce an ensemble method to combine the different summarization algorithms. The ensemble summarization method achieves more than 12.0% improvement over pure-text based methods. 相似文献
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在介绍文本分类技术的基础上,结合学科导航特点,探讨了将文本分类技术应用于学科导航的必要条件,分析了应用文本分类技术后给学科导航带来的影响,通过实证显示了文本分类技术应用于学科导航分类所产生的优势. 相似文献
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借助文本分类系统软件,采用来自10个大类的中文文本数据,按照训练集与测试集2:1的比例,使用KNN和SVM分类算法,对数据集进行自动分类的实验。旨在通过具体的语料库实验,探讨文本自动分类的关键技术,分析、比较与评价实验结果,探讨文本分类中具体参数的设置和不同分类算法之优劣。 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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通过对农业网页的HTML结构和特征研究,叙述基于文本内容的农业网页信息抽取和分类实验研究过程。实验中利用DOM结构对农业网页信息进行信息抽取和预处理,并根据文本的内容自动计算文本类别属性,得到特征词,通过总结样本文档的特征,对遇到的新文档进行自动分类。实验结果表明,本文信息提取的时间复杂度比较小、精确度高,提高了分类的正确率。 相似文献
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We present three fundamental, interrelated approaches to support multiple access paths to each terminal object in information hierarchies: faceted classification, faceted search, and web directories with embedded symbolic links. This survey aims to demonstrate how each approach supports users who seek information from multiple perspectives. We achieve this by exploring each approach, the relationships between these approaches, including tradeoffs, and how they can be used in concert, while focusing on a core set of hypermedia elements common to all. This approach provides a foundation from which to study, understand, and synthesize applications which employ these techniques. This survey does not aim to be comprehensive, but rather focuses on thematic issues. 相似文献