-
Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004)
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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
-
Montesi, M.; Navarrete, T.: Classifying web genres in context : A case study documenting the web genres used by a software engineer (2008)
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- Abstract
- This case study analyzes the Internet-based resources that a software engineer uses in his daily work. Methodologically, we studied the web browser history of the participant, classifying all the web pages he had seen over a period of 12 days into web genres. We interviewed him before and after the analysis of the web browser history. In the first interview, he spoke about his general information behavior; in the second, he commented on each web genre, explaining why and how he used them. As a result, three approaches allow us to describe the set of 23 web genres obtained: (a) the purposes they serve for the participant; (b) the role they play in the various work and search phases; (c) and the way they are used in combination with each other. Further observations concern the way the participant assesses quality of web-based resources, and his information behavior as a software engineer.
- Source
- Information processing and management. 44(2008) no.4, S.1410-1430
-
Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007)
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- Abstract
- The proliferation of digital resources and their integration into a traditional library setting has created a pressing need for an automated tool that organizes textual information based on library classification schemes. Automated text classification is a research field of developing tools, methods, and models to automate text classification. This article describes the current popular approach for text classification and major text classification projects and applications that are based on library classification schemes. Related issues and challenges are discussed, and a number of considerations for the challenges are examined.
- Date
- 22. 9.2008 18:31:54
-
Adams, K.C.: Word wranglers : Automatic classification tools transform enterprise documents from "bags of words" into knowledge resources (2003)
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- Abstract
- Taxonomies are an important part of any knowledge management (KM) system, and automatic classification software is emerging as a "killer app" for consumer and enterprise portals. A number of companies such as Inxight Software , Mohomine, Metacode, and others claim to interpret the semantic content of any textual document and automatically classify text on the fly. The promise that software could automatically produce a Yahoo-style directory is a siren call not many IT managers are able to resist. KM needs have grown more complex due to the increasing amount of digital information, the declining effectiveness of keyword searching, and heterogeneous document formats in corporate databases. This environment requires innovative KM tools, and automatic classification technology is an example of this new kind of software. These products can be divided into three categories according to their underlying technology - rules-based, catalog-by-example, and statistical clustering. Evolving trends in this market include framing classification as a cyborg (computer- and human-based) activity and the increasing use of extensible markup language (XML) and support vector machine (SVM) technology. In this article, we'll survey the rapidly changing automatic classification software market and examine the features and capabilities of leading classification products.
-
Hagedorn, K.; Chapman, S.; Newman, D.: Enhancing search and browse using automated clustering of subject metadata (2007)
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- Abstract
- The Web puzzle of online information resources often hinders end-users from effective and efficient access to these resources. Clustering resources into appropriate subject-based groupings may help alleviate these difficulties, but will it work with heterogeneous material? The University of Michigan and the University of California Irvine joined forces to test automatically enhancing metadata records using the Topic Modeling algorithm on the varied OAIster corpus. We created labels for the resulting clusters of metadata records, matched the clusters to an in-house classification system, and developed a prototype that would showcase methods for search and retrieval using the enhanced records. Results indicated that while the algorithm was somewhat time-intensive to run and using a local classification scheme had its drawbacks, precise clustering of records was achieved and the prototype interface proved that faceted classification could be powerful in helping end-users find resources.
-
Lindholm, J.; Schönthal, T.; Jansson , K.: Experiences of harvesting Web resources in engineering using automatic classification (2003)
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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
-
Liu, R.-L.: Context recognition for hierarchical text classification (2009)
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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
-
Shafer, K.E.: Evaluating Scorpion Results (2001)
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- Abstract
- Using DDC for automatic indexing and classifying of Internet resources
-
Wu, M.; Fuller, M.; Wilkinson, R.: Using clustering and classification approaches in interactive retrieval (2001)
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- Source
- Information processing and management. 37(2001) no.3, S.459-484
-
Subramanian, S.; Shafer, K.E.: Clustering (2001)
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- Date
- 5. 5.2003 14:17:22
-
Major, R.L.; Ragsdale, C.T.: ¬An aggregation approach to the classification problem using multiple prediction experts (2000)
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- Source
- Information processing and management. 36(2000) no.4, S.683-696
-
Golub, K.: Automated subject classification of textual web documents (2006)
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- Abstract
- Purpose - To provide an integrated perspective to similarities and differences between approaches to automated classification in different research communities (machine learning, information retrieval and library science), and point to problems with the approaches and automated classification as such. Design/methodology/approach - A range of works dealing with automated classification of full-text web documents are discussed. Explorations of individual approaches are given in the following sections: special features (description, differences, evaluation), application and characteristics of web pages. Findings - Provides major similarities and differences between the three approaches: document pre-processing and utilization of web-specific document characteristics is common to all the approaches; major differences are in applied algorithms, employment or not of the vector space model and of controlled vocabularies. Problems of automated classification are recognized. Research limitations/implications - The paper does not attempt to provide an exhaustive bibliography of related resources. Practical implications - As an integrated overview of approaches from different research communities with application examples, it is very useful for students in library and information science and computer science, as well as for practitioners. Researchers from one community have the information on how similar tasks are conducted in different communities. Originality/value - To the author's knowledge, no review paper on automated text classification attempted to discuss more than one community's approach from an integrated perspective.
-
Guerrero-Bote, V.P.; Moya Anegón, F. de; Herrero Solana, V.: Document organization using Kohonen's algorithm (2002)
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- Source
- Information processing and management. 38(2002) no.1, S.79-89
-
Automatic classification research at OCLC (2002)
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- Date
- 5. 5.2003 9:22:09
-
Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006)
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- Date
- 22. 7.2006 16:24:52
-
Classification, automation, and new media : Proceedings of the 24th Annual Conference of the Gesellschaft für Klassifikation e.V., University of Passau, March 15 - 17, 2000 (2002)
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- Abstract
- Given the huge amount of information in the internet and in practically every domain of knowledge that we are facing today, knowledge discovery calls for automation. The book deals with methods from classification and data analysis that respond effectively to this rapidly growing challenge. The interested reader will find new methodological insights as well as applications in economics, management science, finance, and marketing, and in pattern recognition, biology, health, and archaeology.
- 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.
-
Miyamoto, S.: Information clustering based an fuzzy multisets (2003)
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- Source
- Information processing and management. 39(2003) no.2, S.195-213
-
Hu, G.; Zhou, S.; Guan, J.; Hu, X.: Towards effective document clustering : a constrained K-means based approach (2008)
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- Source
- Information processing and management. 44(2008) no.4, S.1397-1409
-
Wu, K.J.; Chen, M.-C.; Sun, Y.: Automatic topics discovery from hyperlinked documents (2004)
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- Information processing and management. 40(2004) no.2, S.239-255
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Liu, R.-L.: Dynamic category profiling for text filtering and classification (2007)
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- Information processing and management. 43(2007) no.1, S.154-168
