Document (#36559)

Author

Golub, K.

Title

Automated subject classification of textual documents in the context of Web-based hierarchical browsing

Source

Knowledge organization. 38(2011) no.3, S.230-244

Year

2011

Abstract

While automated methods for information organization have been around for several decades now, exponential growth of the World Wide Web has put them into the forefront of research in different communities, within which several approaches can be identified: 1) machine learning (algorithms that allow computers to improve their performance based on learning from pre-existing data); 2) document clustering (algorithms for unsupervised document organization and automated topic extraction); and 3) string matching (algorithms that match given strings within larger text). Here the aim was to automatically organize textual documents into hierarchical structures for subject browsing. The string-matching approach was tested using a controlled vocabulary (containing pre-selected and pre-defined authorized terms, each corresponding to only one concept). The results imply that an appropriate controlled vocabulary, with a sufficient number of entry terms designating classes, could in itself be a solution for automated classification. Then, if the same controlled vocabulary had an appropriat hierarchical structure, it would at the same time provide a good browsing structure for the collection of automatically classified documents.

Content

Vgl.: http://www.ergon-verlag.de/isko_ko/downloads/ko_38_2011_3_d.pdf.

Theme

Automatisches Klassifizieren

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4. Golub, K.; Lykke, M.: Automated classification of web pages in hierarchical browsing (2009) 0.27

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5. Chen, H.; Houston, A.L.; Sewell, R.R.; Schatz, B.R.: Internet browsing and searching : user evaluations of category map and concept space techniques (1998) 0.23

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