共查询到20条相似文献，搜索用时 31 毫秒
1.
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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In this paper, a Generalized Cluster Centroid based Classifier (GCCC) and its variants for text categorization are proposed by utilizing a clustering algorithm to integrate two well-known classifiers, i.e., the K-nearest-neighbor (KNN) classifier and the Rocchio classifier. KNN, a lazy learning method, suffers from inefficiency in online categorization while achieving remarkable effectiveness. Rocchio, which has efficient categorization performance, fails to obtain an expressive categorization model due to its inherent linear separability assumption. Our proposed method mainly focuses on two points: one point is that we use a clustering algorithm to strengthen the expressiveness of the Rocchio model; another one is that we employ the improved Rocchio model to speed up the categorization process of KNN. Extensive experiments conducted on both English and Chinese corpora show that GCCC and its variants have better categorization ability than some state-of-the-art classifiers, i.e., Rocchio, KNN and Support Vector Machine (SVM). 相似文献
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Bassam Al-Salemi Masri Ayob Graham Kendall Shahrul Azman Mohd Noah 《Information processing & management》2019,56(1):212-227
Multi-label text categorization refers to the problem of assigning each document to a subset of categories by means of multi-label learning algorithms. Unlike English and most other languages, the unavailability of Arabic benchmark datasets prevents evaluating multi-label learning algorithms for Arabic text categorization. As a result, only a few recent studies have dealt with multi-label Arabic text categorization on non-benchmark and inaccessible datasets. Therefore, this work aims to promote multi-label Arabic text categorization through (a) introducing “RTAnews”, a new benchmark dataset of multi-label Arabic news articles for text categorization and other supervised learning tasks. The benchmark is publicly available in several formats compatible with the existing multi-label learning tools, such as MEKA and Mulan. (b) Conducting an extensive comparison of most of the well-known multi-label learning algorithms for Arabic text categorization in order to have baseline results and show the effectiveness of these algorithms for Arabic text categorization on RTAnews. The evaluation involves four multi-label transformation-based algorithms: Binary Relevance, Classifier Chains, Calibrated Ranking by Pairwise Comparison and Label Powerset, with three base learners (Support Vector Machine, k-Nearest-Neighbors and Random Forest); and four adaptation-based algorithms (Multi-label kNN, Instance-Based Learning by Logistic Regression Multi-label, Binary Relevance kNN and RFBoost). The reported baseline results show that both RFBoost and Label Powerset with Support Vector Machine as base learner outperformed other compared algorithms. Results also demonstrated that adaptation-based algorithms are faster than transformation-based algorithms. 相似文献
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Modern web search engines are expected to return the top-k results efficiently. Although many dynamic index pruning strategies have been proposed for efficient top-k computation, most of them are prone to ignoring some especially important factors in ranking functions, such as term-proximity (the distance relationship between query terms in a document). In our recent work [Zhu, M., Shi, S., Li, M., & Wen, J. (2007). Effective top-k computation in retrieving structured documents with term-proximity support. In Proceedings of 16th CIKM conference (pp. 771–780)], we demonstrated that, when term-proximity is incorporated into ranking functions, most existing index structures and top-k strategies become quite inefficient. To solve this problem, we built the inverted index based on web page structure and proposed the query processing strategies accordingly. The experimental results indicate that the proposed index structures and query processing strategies significantly improve the top-k efficiency. In this paper, we study the possibility of adopting additional techniques to further improve top-k computation efficiency. We propose a Proximity-Probe Heuristic to make our top-k algorithms more efficient. We also test the efficiency of our approaches on various settings (linear or non-linear ranking functions, exact or approximate top-k processing, etc.). 相似文献
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一种基于k最近邻的快速文本分类方法 总被引：5，自引：0，他引：5
k最近邻方法是一种简单而有效的文本分类方法,但是传统的k最近邻分类方法在搜索k个最近邻时需要高强度的相似性计算,尤其是在训练集数据量很大情况下,全局的最优搜索几乎是不可能的.因此,加速k个最近邻的搜索是k最近邻方法实用的关键.本文提出了一种基于k最近邻的快速文本分类方法,它能够保证在海量数据集中进行快速有效的分类.实验结果表明这一方法较传统方法性能有显著提升. 相似文献
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Cross-lingual text categorization: Conquering language boundaries in globalized environments 总被引：1，自引：0，他引：1
Text categorization pertains to the automatic learning of a text categorization model from a training set of preclassified documents on the basis of their contents and the subsequent assignment of unclassified documents to appropriate categories. Most existing text categorization techniques deal with monolingual documents (i.e., written in the same language) during the learning of the text categorization model and category assignment (or prediction) for unclassified documents. However, with the globalization of business environments and advances in Internet technology, an organization or individual may generate and organize into categories documents in one language and subsequently archive documents in different languages into existing categories, which necessitate cross-lingual text categorization (CLTC). Specifically, cross-lingual text categorization deals with learning a text categorization model from a set of training documents written in one language (e.g., L1) and then classifying new documents in a different language (e.g., L2). Motivated by the significance of this demand, this study aims to design a CLTC technique with two different category assignment methods, namely, individual- and cluster-based. Using monolingual text categorization as a performance reference, our empirical evaluation results demonstrate the cross-lingual capability of the proposed CLTC technique. Moreover, the classification accuracy achieved by the cluster-based category assignment method is statistically significantly higher than that attained by the individual-based method. 相似文献
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Louis-Gavet Guy 《Information processing & management》1977,13(3):189-200
In this paper we demonstrate a new method for concentrating the set of key-words of a thesaurus. This method is based on a mathematical study that we have carried out into the distribution of characters in a defined natural language.We have built a function f of concentration which generates only a few synonyms. In applying this function to the set of key-words of a thesaurus, we reduce each key-word to four characters without synonymity. (For three characters we have a rate of synonymity of approx. 1/1000th.)A new structure of binary files allows the thesaurus to be contained in a table of less than 700 bytes. 相似文献
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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
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本文对叙词表向本体的转换做了综合性的讨论,并提出了一种基于概念的叙词表转换方法,从而实现叙词表的RDF/OWL描述。 相似文献
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A proposed particle swarm classifier has been integrated with the concept of intelligently controlling the search process of PSO to develop an efficient swarm intelligence based classifier, which is called intelligent particle swarm classifier (IPS-classifier). This classifier is described to find the decision hyperplanes to classify patterns of different classes in the feature space. An intelligent fuzzy controller is designed to improve the performance and efficiency of the proposed classifier by adapting three important parameters of PSO (inertia weight, cognitive parameter and social parameter). Three pattern recognition problems with different feature vector dimensions are used to demonstrate the effectiveness of the introduced classifier: Iris data classification, Wine data classification and radar targets classification from backscattered signals. The experimental results show that the performance of the IPS-classifier is comparable to or better than the k-nearest neighbor (k-NN) and multi-layer perceptron (MLP) classifiers, which are two conventional classifiers. 相似文献
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词表是图书馆和信息检索领域最重要的知识组织工具,《中国分类主题词表》是传统词表的一种,它的更新和维护一直依靠手工进行,这制约了它在数字图书馆和网络信息环境下的应用。本文介绍了一项基于统计的、从元数据的标题中抽取关键词并定位在词表中的方法。大致包括三个步骤:从标题中提取关键词;确定抽取出的关键词的专指度;将专指度高的专业词汇定位在词表中。在《中国分类主题词表》和上海图书馆提供的计算机科技领域的元数据上所进行实验,结果证明该方法是可行的。这一方法可以应用到自动标引或编目中,有一定的实用性和广阔的应用前景。 相似文献
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《Information processing & management》1999,35(5):655-678
In this paper we show that in advanced information retrieval (IR) applications capabilities for data aggregation, transitive computation and NF2 (non-first normal form) relational computation are often necessary at the same time. We demonstrate that complex objects are naturally modeled as NF2 relations, whereas structures like hierarchical thesauri must be modeled for transitive computation. Transitive processing cannot be supported by structurally static structures like NF2 relations. We present a truly declarative query interface, which integrates data aggregation, transitive computation and NF2 relational computation. Thus the interface supports the retrieval and structural manipulation of complex objects (e.g. documents and bibliographic references), their retrieval through transitive relationships (e.g. thesauri, citations) and data aggregation based on their components (e.g. citation counts, author productivity). Most importantly, users can formulate queries on a high abstraction level without mastering actual programming or database techniques. 相似文献
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Decisions in thesaurus construction and use 总被引：1，自引：0，他引：1
A thesaurus and an ontology provide a set of structured terms, phrases, and metadata, often in a hierarchical arrangement, that may be used to index, search, and mine documents. We describe the decisions that should be made when including a term, deciding whether a term should be subdivided into its subclasses, or determining which of more than one set of possible subclasses should be used. Based on retrospective measurements or estimates of future performance when using thesaurus terms in document ordering, decisions are made so as to maximize performance. These decisions may be used in the automatic construction of a thesaurus. The evaluation of an existing thesaurus is described, consistent with the decision criteria developed here. These kinds of user-focused decision-theoretic techniques may be applied to other hierarchical applications, such as faceted classification systems used in information architecture or the use of hierarchical terms in “breadcrumb navigation”. 相似文献
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One of the best known measures of information retrieval (IR) performance is the F-score, the harmonic mean of precision and recall. In this article we show that the curve of the F-score as a function of the number of retrieved items is always of the same shape: a fast concave increase to a maximum, followed by a slow decrease. In other words, there exists a single maximum, referred to as the tipping point, where the retrieval situation is ‘ideal’ in terms of the F-score. The tipping point thus indicates the optimal number of items to be retrieved, with more or less items resulting in a lower F-score. This empirical result is found in IR and link prediction experiments and can be partially explained theoretically, expanding on earlier results by Egghe. We discuss the implications and argue that, when comparing F-scores, one should compare the F-score curves’ tipping points. 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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El-Sayed Atlam Ghada ElmarhomyAuthor VitaeKazuhiro MoritaAuthor Vitae Masao FuketaAuthor VitaeJun-ichi AoeAuthor Vitae 《Information processing & management》2006
With increasing popularity of the Internet and tremendous amount of on-line text, automatic document classification is important for organizing huge amounts of data. Readers can know the subject of many document fields by reading only some specific Field Association (FA) words. Document fields can be decided efficiently if there are many FA words and if the frequency rate is high. This paper proposes a method for automatically building new FA words. A WWW search engine is used to extract FA word candidates from document corpora. New FA word candidates in each field are automatically compared with previously determined FA words. Then new FA words are appended to an FA word dictionary. From the experiential results, our new system can automatically appended around 44% of new FA words to the existence FA word dictionary. Moreover, the concentration ratio 0.9 is also effective for extracting relevant FA words that needed for the system design to build FA words automatically. 相似文献
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In this paper, we consider the problem of selecting a subset of k systems that is contained in the set of the best s simulated systems when the number of alternative systems is huge. We propose a sequential method that uses the ordinal optimization to select a subset G randomly from the search space that contains the best simulated systems with high probability. To guarantee that this subset contains the best systems it needs to be relatively large. Then methods of ranking and selections will be applied to select a subset of k best systems of the subset G with high probability. The remaining systems of G will be replaced by newly selected alternatives from the search space. This procedure is repeated until the probability of correct selection (a subset of the best k simulated systems is selected) becomes very high. The optimal computing budget allocation is also used to allocate the available computing budget in a way that maximizes the probability of correct selection. Numerical experiments for comparing these algorithms are presented. 相似文献