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1.
Webpages are mainly distinguished by their topic (e.g., politics, sports etc.) and genre (e.g., blogs, homepages, e-shops, etc.). Automatic detection of webpage genre could considerably enhance the ability of modern search engines to focus on the requirements of the user’s information need. In this paper, we present an approach to webpage genre detection based on a fully-automated extraction of the feature set that represents the style of webpages. The features we propose (character n-grams of variable length and HTML tags) are language-independent and easily-extracted while they can be adapted to the properties of the still evolving web genres and the noisy environment of the web. Experiments based on two publicly-available corpora show that the performance of the proposed approach is superior in comparison to previously reported results. It is also shown that character n-grams are better features than words when the dimensionality increases while the binary representation is more effective than the term-frequency representation for both feature types. Moreover, we perform a series of cross-check experiments (e.g., training using a genre palette and testing using a different genre palette as well as using the features extracted from one corpus to discriminate the genres of the other corpus) to illustrate the robustness of our approach and its ability to capture the general stylistic properties of genre categories even when the feature set is not optimized for the given corpus. 相似文献
2.
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
3.
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
4.
Chien-Liang Liu Wen-Hoar HsaioChia-Hoang Lee Chun-Hsien Chen 《Information processing & management》2013
This study employs our proposed semi-supervised clustering method called Constrained-PLSA to cluster tagged documents with a small amount of labeled documents and uses two data sets for system performance evaluations. The first data set is a document set whose boundaries among the clusters are not clear; while the second one has clear boundaries among clusters. This study employs abstracts of papers and the tags annotated by users to cluster documents. Four combinations of tags and words are used for feature representations. The experimental results indicate that almost all of the methods can benefit from tags. However, unsupervised learning methods fail to function properly in the data set with noisy information, but Constrained-PLSA functions properly. In many real applications, background knowledge is ready, making it appropriate to employ background knowledge in the clustering process to make the learning more fast and effective. 相似文献
5.
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
6.
Search systems are limited by their inability to distinguish between information that is on topic and information that is useful, i.e. suitable and applicable to the tasks at hand. This paper presents the results of two studies that examine a possible approach to identifying more useful documents through the relationships between searchers’ tasks and the document genres in the collection. A questionnaire and an experimental user study conducted in two domains, provide evidence that perceptions of usefulness are dependent upon information task type, document genre, and the relationship between these two factors. Expertise is also found to have an effect on usefulness. These results further our understanding of the role of task and genre interactive information retrieval. 相似文献
7.
Frequent requests from users to search engines on the World Wide Web are to search for information about people using personal names. Current search engines only return sets of documents containing the name queried, but, as several people usually share a personal name, the resulting sets often contain documents relevant to several people. It is necessary to disambiguate people in these result sets in order to to help users find the person of interest more readily. In the task of name disambiguation, effective measurement of similarities in the documents is a crucial step towards the final disambiguation. We propose a new method that uses web directories as a knowledge base to find common contexts in documents and uses the common contexts measure to determine document similarities. Experiments, conducted on documents mentioning real people on the web, together with several famous web directory structures, suggest that there are significant advantages in using web directories to disambiguate people compared with other conventional methods. 相似文献
8.
The number of patent documents is currently rising rapidly worldwide, creating the need for an automatic categorization system to replace time-consuming and labor-intensive manual categorization. Because accurate patent classification is crucial to search for relevant existing patents in a certain field, patent categorization is a very important and useful field. As patent documents are structural documents with their own characteristics distinguished from general documents, these unique traits should be considered in the patent categorization process. In this paper, we categorize Japanese patent documents automatically, focusing on their characteristics: patents are structured by claims, purposes, effects, embodiments of the invention, and so on. We propose a patent document categorization method that uses the k-NN (k-Nearest Neighbour) approach. In order to retrieve similar documents from a training document set, some specific components to denote the so-called semantic elements, such as claim, purpose, and application field, are compared instead of the whole texts. Because those specific components are identified by various user-defined tags, first all of the components are clustered into several semantic elements. Such semantically clustered structural components are the basic features of patent categorization. We can achieve a 74% improvement of categorization performance over a baseline system that does not use the structural information of the patent. 相似文献
9.
文本聚类算法的质量评价 总被引：4，自引：0，他引：4
文本聚类是建立大规模文本集合的分类体系实例的有效手段之一。本文讨论了利用标准的分类测试集合进行聚类质量的量化评价的手段，选择了k-Means聚类算法、STC（后缀树聚类）算法和基于Ant的聚类算法进行了实验对比。对实验结果的分析表明，STC聚类算法由于在处理文本时充分考虑了文本的短语特性，其聚类效果较好；基于Ant的聚类算法的结果受参数输入的影响较大；在Ant聚类算法中引入文本特性可以提高聚类结果的质量。 相似文献
10.
Yewei Xue Yunhua Hu Guomao Xin Ruihua Song Shuming Shi Yunbo Cao Chin-Yew Lin Hang Li 《Information processing & management》2007
This paper is concerned with automatic extraction of titles from the bodies of HTML documents (web pages). Titles of HTML documents should be correctly defined in the title fields by the authors; however, in reality they are often bogus. It is advantageous if we can automatically extract titles from HTML documents. In this paper, we take a supervised machine learning approach to address the problem. We first propose a specification on HTML titles, that is, a ‘definition’ on HTML titles. Next, we employ two learning methods to perform the task. In one method, we utilize features extracted from the DOM (direct object model) Tree; in the other method, we utilize features based on vision. We also combine the two methods to further enhance the extraction accuracy. Our title extraction methods significantly outperform the baseline method of using the lines in largest font size as title (22.6–37.4% improvements in terms of F1 score). As application, we consider web page retrieval. We use the TREC Web Track data for evaluation. We propose a new method for HTML documents retrieval using extracted titles. Experimental results indicate that the use of both extracted titles and title fields is almost always better than the use of title fields alone; the use of extracted titles is particularly helpful in the task of named page finding (25.1–30.3% improvements). 相似文献
11.
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
12.
Tadeusz Radecki 《Information processing & management》1977,13(2):109-116
Search patterns of documents and information requests are their better or worse representatives only, so it is important to carry on examinations on possibilities of designing self-learning information retrieval systems. Another important question is to elaborate such an organization of document search pattern set as to obtain an acceptable response time of the information system to a given information request.A self-learning process of the proposed information system consists in the determination—on a set of document and information request search patterns—of the similarity relation according to L. A. Zadeh.The organization of a set of document search patterns proposed in the paper ensures the limitation of document search pattern set searching process—when retrieving a response to a given information request—to one (or several) subset from previously determined subsets. This makes the information system response time acceptable. The proposed information retrieval strategy is discussed in terms of fuzzy sets. 相似文献
13.
Transfer learning utilizes labeled data available from some related domain (source domain) for achieving effective knowledge transformation to the target domain. However, most state-of-the-art cross-domain classification methods treat documents as plain text and ignore the hyperlink (or citation) relationship existing among the documents. In this paper, we propose a novel cross-domain document classification approach called Link-Bridged Topic model (LBT). LBT consists of two key steps. Firstly, LBT utilizes an auxiliary link network to discover the direct or indirect co-citation relationship among documents by embedding the background knowledge into a graph kernel. The mined co-citation relationship is leveraged to bridge the gap across different domains. Secondly, LBT simultaneously combines the content information and link structures into a unified latent topic model. The model is based on an assumption that the documents of source and target domains share some common topics from the point of view of both content information and link structure. By mapping both domains data into the latent topic spaces, LBT encodes the knowledge about domain commonality and difference as the shared topics with associated differential probabilities. The learned latent topics must be consistent with the source and target data, as well as content and link statistics. Then the shared topics act as the bridge to facilitate knowledge transfer from the source to the target domains. Experiments on different types of datasets show that our algorithm significantly improves the generalization performance of cross-domain document classification. 相似文献
14.
We present a new paradigm for the automatic creation of document headlines that is based on direct transformation of relevant textual information into well-formed textual output. Starting from an input document, we automatically create compact representations of weighted finite sets of strings, called WIDL-expressions, which encode the most important topics in the document. A generic natural language generation engine performs the headline generation task, driven by both statistical knowledge encapsulated in WIDL-expressions (representing topic biases induced by the input document) and statistical knowledge encapsulated in language models (representing biases induced by the target language). Our evaluation shows similar performance in quality with a state-of-the-art, extractive approach to headline generation, and significant improvements in quality over previously proposed solutions to abstractive headline generation. 相似文献
15.
16.
《Information processing & management》2005,41(4):1003-1018
The authors introduce an information visualization model, WebStar, for hyperlink-based information systems. Hyperlinks within a hyperlink-based document can be visualized in a two-dimensional visual space. All links are projected within a display sphere in the visual space. The relationship between a specified central document and its hyperlinked documents is visually presented in the visual space. In addition, users are able to define a group of subjects and to observe relevance between each subject and all hyperlinked documents via movement of that subject around the display sphere center. WebStar allows users to dynamically change an interest center during navigation. A retrieval mechanism is developed to control retrieved results in the visual space. Impact of movement of a subject on the visual document distribution is analyzed. An ambiguity problem caused by projection is discussed. Potential applications of this visualization model in information retrieval are included. Future research directions on the topic are addressed. 相似文献
17.
《Information processing & management》2023,60(4):103363
Government document analysis can help organizations to understand key policy points and make right response. To realize intelligent analysis of a volume of policies, we propose a discourse parsing technique for in-depth understanding of Chinese government documents (CGDs). Based on Superstructure Schema and Rhetorical Structure Theory (RST), we characterize the stylistic features and macrostructure patterns of CGDs and develop a discourse analysis framework to specify its functional structure and semantic system. A tree schema is adopted to portray the hierarchy of textual fragments in full text and formalize their representations in machine-based parsing, then a multi-head partition self-attention model is constructed to evaluate the discourse analysis framework. Experiment results show that our parsing model embedding with inherent CGD discourse features performs better than baselines. The highest f1-score in span and label prediction tasks researches 0.9209. The parsing model can be migrated to other discourse parsing tasks by considering specific genre features of different documents. 相似文献
18.
《Information processing & management》2020,57(3):102216
Learning semantic representations of documents is essential for various downstream applications, including text classification and information retrieval. Entities, as important sources of information, have been playing a crucial role in assisting latent representations of documents. In this work, we hypothesize that entities are not monolithic concepts; instead they have multiple aspects, and different documents may be discussing different aspects of a given entity. Given that, we argue that from an entity-centric point of view, a document related to multiple entities shall be (a) represented differently for different entities (multiple entity-centric representations), and (b) each entity-centric representation should reflect the specific aspects of the entity discussed in the document.In this work, we devise the following research questions: (1) Can we confirm that entities have multiple aspects, with different aspects reflected in different documents, (2) can we learn a representation of entity aspects from a collection of documents, and a representation of document based on the multiple entities and their aspects as reflected in the documents, (3) does this novel representation improves algorithm performance in downstream applications, and (4) what is a reasonable number of aspects per entity? To answer these questions we model each entity using multiple aspects (entity facets1), where each entity facet is represented as a mixture of latent topics. Then, given a document associated with multiple entities, we assume multiple entity-centric representations, where each entity-centric representation is a mixture of entity facets for each entity. Finally, a novel graphical model, the Entity Facet Topic Model (EFTM), is proposed in order to learn entity-centric document representations, entity facets, and latent topics.Through experimentation we confirm that (1) entities are multi-faceted concepts which we can model and learn, (2) a multi-faceted entity-centric modeling of documents can lead to effective representations, which (3) can have an impact in downstream application, and (4) considering a small number of facets is effective enough. In particular, we visualize entity facets within a set of documents, and demonstrate that indeed different sets of documents reflect different facets of entities. Further, we demonstrate that the proposed entity facet topic model generates better document representations in terms of perplexity, compared to state-of-the-art document representation methods. Moreover, we show that the proposed model outperforms baseline methods in the application of multi-label classification. Finally, we study the impact of EFTM’s parameters and find that a small number of facets better captures entity specific topics, which confirms the intuition that on average an entity has a small number of facets reflected in documents. 相似文献
20.
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