Search (81 results, page 1 of 5)

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Abstract

Information is often organized as a text hierarchy. A hierarchical text-classification system is thus essential for the management, sharing, and dissemination of information. It aims to automatically classify each incoming document into zero, one, or several categories in the text hierarchy. In this paper, we present a technique called CRHTC (context recognition for hierarchical text classification) that performs hierarchical text classification by recognizing the context of discussion (COD) of each category. A category's COD is governed by its ancestor categories, whose contents indicate contextual backgrounds of the category. A document may be classified into a category only if its content matches the category's COD. CRHTC does not require any trials to manually set parameters, and hence is more portable and easier to implement than other methods. It is empirically evaluated under various conditions. The results show that CRHTC achieves both better and more stable performance than several hierarchical and nonhierarchical text-classification methodologies.

Date

22. 3.2009 19:11:54

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Abstract

Purpose - The purpose of this study is twofold: to investigate whether it is meaningful to use the Engineering Index (Ei) classification scheme for browsing, and then, if proven useful, to investigate the performance of an automated classification algorithm based on the Ei classification scheme. Design/methodology/approach - A user study was conducted in which users solved four controlled searching tasks. The users browsed the Ei classification scheme in order to examine the suitability of the classification systems for browsing. The classification algorithm was evaluated by the users who judged the correctness of the automatically assigned classes. Findings - The study showed that the Ei classification scheme is suited for browsing. Automatically assigned classes were on average partly correct, with some classes working better than others. Success of browsing showed to be correlated and dependent on classification correctness. Research limitations/implications - Further research should address problems of disparate evaluations of one and the same web page. Additional reasons behind browsing failures in the Ei classification scheme also need further investigation. Practical implications - Improvements for browsing were identified: describing class captions and/or listing their subclasses from start; allowing for searching for words from class captions with synonym search (easily provided for Ei since the classes are mapped to thesauri terms); when searching for class captions, returning the hierarchical tree expanded around the class in which caption the search term is found. The need for improvements of classification schemes was also indicated. Originality/value - A user-based evaluation of automated subject classification in the context of browsing has not been conducted before; hence the study also presents new findings concerning methodology.

Object

Engineering Index Classification

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Abstract

The Nordic WAIS/WWW project sponsored by NORDINFO is a joint project between Lund University Library and the National Technological Library of Denmark. It aims to improve the existing networked information discovery and retrieval tools Wide Area Information System (WAIS) and World Wide Web (WWW), and to move towards unifying WWW and WAIS. Details current results focusing on the WAIS side of the project. Describes research into automatic indexing and classification of WAIS sources, development of an orientation tool for WAIS, and development of a WAIS index of WWW resources

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Content

Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.

Date

8. 1.2013 10:22:32

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Abstract

In text categorization tasks, classification on some class hierarchies has better results than in cases without the hierarchy. Currently, because a large number of documents are divided into several subgroups in a hierarchy, we can appropriately use a hierarchical classification method. However, we have no systematic method to build a hierarchical classification system that performs well with large collections of practical data. In this article, we introduce a new evaluation scheme for internal node classifiers, which can be used effectively to develop a hierarchical classification system. We also show that our method for constructing the hierarchical classification system is very effective, especially for the task of constructing classifiers applied to hierarchy tree with a lot of levels.

Date

22. 7.2006 16:24:52

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Abstract

The goal of this research is to clarify the role of document classification in information filtering. An important function of classification, in managing computational complexity, is described and illustrated in the context of an existing filtering system. A parameter called classification homogeneity is presented for analyzing unsupervised automated classification by employing human classification as a control. 2 significant components of the automated classification approach, vocabulary discovery and classification scheme generation, are described in detail. Results of classification performance revealed considerable variability in the homogeneity of automatically produced classes. Based on the classification performance, different types of interest profiles were created. Subsequently, these profiles were used to perform filtering sessions. The filtering results showed that with increasing homogeneity, filtering performance improves, and, conversely, with decreasing homogeneity, filtering performance degrades

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Abstract

Visualization interfaces can improve subject access by highlighting the inclusion of document representation components in similarity and discrimination relationships. Within a set of retrieved documents, what kinds of groupings can index terms and subject headings make explicit? The role of controlled vocabulary in classifying search output is examined

Date

22. 9.1997 19:16:05

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Abstract

The selection in this reader by R. M. Needham and K. Sparck Jones reports an early approach to automatic classification that was taken in England. The following selection reviews various approaches that were being pursued in the United States at about the same time. It then discusses a particular approach initiated in the early 1960s by Harold Borko, at that time Head of the Language Processing and Retrieval Research Staff at the System Development Corporation, Santa Monica, California and, since 1966, a member of the faculty at the Graduate School of Library and Information Science, University of California, Los Angeles. As was described earlier, there are two steps in automatic classification, the first being to identify pairs of terms that are similar by virtue of co-occurring as index terms in the same documents, and the second being to form equivalence classes of intersubstitutable terms. To compute similarities, Borko and his associates used a standard correlation formula; to derive classification categories, where Needham and Sparck Jones used clumping, the Borko team used the statistical technique of factor analysis. The fact that documents can be classified automatically, and in any number of ways, is worthy of passing notice. Worthy of serious attention would be a demonstra tion that a computer-based classification system was effective in the organization and retrieval of documents. One reason for the inclusion of the following selection in the reader is that it addresses the question of evaluation. To evaluate the effectiveness of their automatically derived classification, Borko and his team asked three questions. The first was Is the classification reliable? in other words, could the categories derived from one sample of texts be used to classify other texts? Reliability was assessed by a case-study comparison of the classes derived from three different samples of abstracts. The notso-surprising conclusion reached was that automatically derived classes were reliable only to the extent that the sample from which they were derived was representative of the total document collection. The second evaluation question asked whether the classification was reasonable, in the sense of adequately describing the content of the document collection. The answer was sought by comparing the automatically derived categories with categories in a related classification system that was manually constructed. Here the conclusion was that the automatic method yielded categories that fairly accurately reflected the major area of interest in the sample collection of texts; however, since there were only eleven such categories and they were quite broad, they could not be regarded as suitable for use in a university or any large general library. The third evaluation question asked whether automatic classification was accurate, in the sense of producing results similar to those obtainabie by human cIassifiers. When using human classification as a criterion, automatic classification was found to be 50 percent accurate.

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Abstract

Automatic text classification (TC) is essential for the management of information. To properly classify a document d, it is essential to identify the semantics of each term t in d, while the semantics heavily depend on context (neighboring terms) of t in d. Therefore, we present a technique CTFA (Context-based Term Frequency Assessment) that improves text classifiers by considering term contexts in test documents. The results of the term context recognition are used to assess term frequencies of terms, and hence CTFA may easily work with various kinds of text classifiers that base their TC decisions on term frequencies, without needing to modify the classifiers. Moreover, CTFA is efficient, and neither huge memory nor domain-specific knowledge is required. Empirical results show that CTFA successfully enhances performance of several kinds of text classifiers on different experimental data.

Object

Context-based Term Frequency Assessment

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Source

Records management quarterly. 28(1994) no.3, S.20-29

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Abstract

This study developed a specialized directory system using an automatic classification technique. Economics was selected as the subject field for the classification experiments with Web documents. The classification scheme of the directory follows the DDC, and subject terms representing each class number or subject category were selected from the DDC table to construct a representative term dictionary. In collecting and classifying the Web documents, various strategies were tested in order to find the optimal thresholds. In the classification experiments, Web documents in economics were classified into a total of 757 hierarchical subject categories built from the DDC scheme. The first and second experiments using the representative term dictionary resulted in relatively high precision ratios of 77 and 60%, respectively. The third experiment employing a machine learning-based k-nearest neighbours (kNN) classifier in a closed experimental setting achieved a precision ratio of 96%. This implies that it is possible to enhance the classification performance by applying a hybrid method combining a dictionary-based technique and a kNN classifier

Source

Journal of information science. 29(2003) no.2, S.117-126

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Abstract

Authors describe the background and the work involved in setting up Engine-e, a Web index that uses automatic classification as a mean for the selection of resources in Engineering. Considerations in offering a robot-generated Web index as a successor to a manually indexed quality-controlled subject gateway are also discussed

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Abstract

Since most popular commercialized systems for full text retrieval are designed with full text scaning and Boolean logic query mode, these systems use an oversimplified relationship between the indexing form and the content of document. Reports the use of Singular Value Decomposition (SVD) to develop a Cluster Indexing Model (CIM) based on a Vector Space Model (VSM) in orer to explore the index theory of cluster indexing for chinese full text documents. From a series of experiments, it was found that the indexing performance of CIM is better than traditional VSM, and has almost equivalent effectiveness of the authority control of index terms

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Date

22. 1.2010 14:41:24

Footnote

Vortrag gehalten am 03.06.2009 auf dem 98. Bibliothekartag 2009 in Erfurt; erscheint in: Dialog mit Biliotheken. Vgl. auch: http://www.gbv.de/vgm/info/biblio/01VZG/06Publikationen/2009/index.

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Content

"LCI Comprend ist ab sofort als integriertes Modul für EMCs Content Management System Documentum verfügbar. LCI (Learning Computers International GmbH) hat mit Unterstützung von neeb & partner diese Technologie zur Dokumentenautomation transparent in Documentum integriert. Dies ist die erste bekannte Lösung für automatische, lernende Klassifikation und Extraktion, die direkt auf dem Documentum Datenbestand arbeitet und ohne zusätzliche externe Steuerung auskommt. Die LCI Information Capture Services (ICS) dienen dazu, jegliche Art von Dokument zu klassifizieren und Information daraus zu extrahieren. Das Dokument kann strukturiert, halbstrukturiert oder unstrukturiert sein. Somit können beispielsweise gescannte Formulare genauso verarbeitet werden wie Rechnungen oder E-Mails. Die Extraktions- und Klassifikationsvorschriften und die zu lernenden Beispieldokumente werden einfach interaktiv zusammengestellt und als XML-Struktur gespeichert. Zur Laufzeit wird das Projekt angewendet, um unbekannte Dokumente aufgrund von Regeln und gelernten Beispielen automatisch zu indexieren. Dokumente können damit entweder innerhalb von Documentum oder während des Imports verarbeitet werden. Der neue Server erlaubt das Einlesen von Dateien aus dem Dateisystem oder direkt von POPS-Konten, die Analyse der Dokumente und die automatische Erzeugung von Indexwerten bei der Speicherung in einer Documentum Ablageumgebung. Diese Indexwerte, die durch inhaltsbasierte, auch mehrthematische Klassifikation oder durch Extraktion gewonnen wurden, werden als vordefinierte Attribute mit dem Documentum-Objekt abgelegt. Handelt es sich um ein gescanntes Dokument oder ein Fax, wird automatisch die integrierte Volltext-Texterkennung durchgeführt."

Footnote

Kontakt: LCI GmbH, Freiburger Str. 16, 16,79199 Kirchzarten, Tel.: (0 76 61) 9 89 961o, Fax: (01212) 5 37 48 29 36, info@lci-software.com, www.lci-software.com

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Abstract

Automatic categorization is a viable method to deal with the scaling problem on the World Wide Web. For Web site classification, this paper proposes the use of Web pages linked with the home page in a different manner from the sole use of home pages in previous research. To implement our proposed method, we derive a scheme for Web site classification based on the k-nearest neighbor (k-NN) approach. It consists of three phases: Web page selection (connectivity analysis), Web page classification, and Web site classification. Given a Web site, the Web page selection chooses several representative Web pages using connectivity analysis. The k-NN classifier next classifies each of the selected Web pages. Finally, the classified Web pages are extended to a classification of the entire Web site. To improve performance, we supplement the k-NN approach with a feature selection method and a term weighting scheme using markup tags, and also reform its document-document similarity measure. In our experiments on a Korean commercial Web directory, the proposed system, using both a home page and its linked pages, improved the performance of micro-averaging breakeven point by 30.02%, compared with an ordinary classification which uses a home page only.

Date

27.12.2007 17:32:29

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Content

Data Analysis, Statistics, and Classification.- Pattern Recognition and Automation.- Data Mining, Information Processing, and Automation.- New Media, Web Mining, and Automation.- Applications in Management Science, Finance, and Marketing.- Applications in Medicine, Biology, Archaeology, and Others.- Author Index.- Subject Index.

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Abstract

This paper presents a two-phased research project aiming to improve email triage for public administration managers. The first phase developed a typology of email classification patterns through a qualitative study involving 34 participants. Inspired by the fields of pragmatics and speech act theory, this typology comprising four top level categories and 13 subcategories represents the typical email triage behaviors of managers in an organizational context. The second study phase was conducted on a corpus of 1,703 messages using email samples of two managers. Using the k-NN (k-nearest neighbor) algorithm, statistical treatments automatically classified the email according to lexical and nonlexical features representative of managers' triage patterns. The automatic classification of email according to the lexicon of the messages was found to be substantially more efficient when k = 2 and n = 2,000. For four categories, the average recall rate was 94.32%, the average precision rate was 94.50%, and the accuracy rate was 94.54%. For 13 categories, the average recall rate was 91.09%, the average precision rate was 84.18%, and the accuracy rate was 88.70%. It appears that a message's nonlexical features are also deeply influenced by email pragmatics. Features related to the recipient and the sender were the most relevant for characterizing email.

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Abstract

Tools for automatic subject assignment help deal with scale and sustainability in creating and enriching metadata, establishing more connections across and between resources and enhancing consistency. Although some software vendors and experimental researchers claim the tools can replace manual subject indexing, hard scientific evidence of their performance in operating information environments is scarce. A major reason for this is that research is usually conducted in laboratory conditions, excluding the complexities of real-life systems and situations. The article reviews and discusses issues with existing evaluation approaches such as problems of aboutness and relevance assessments, implying the need to use more than a single "gold standard" method when evaluating indexing and retrieval, and proposes a comprehensive evaluation framework. The framework is informed by a systematic review of the literature on evaluation approaches: evaluating indexing quality directly through assessment by an evaluator or through comparison with a gold standard, evaluating the quality of computer-assisted indexing directly in the context of an indexing workflow, and evaluating indexing quality indirectly through analyzing retrieval performance.