Automated Subject Classification of Textual Documents in the Context of Web-Based Hierarchical Browsing
Abstract: With the exponential growth of the World Wide Web, automated subject classification has become a major research issue. Organizing web pages into a hierarchical structure for subject browsing has been gaining more recognition as an important tool in information-seeking processes.The most frequent approach to automated classification is machine learning. It, however, requires training documents and performs well on new documents only if they are similar enough to the former. In the thesis, a string-matching algorithm based on a controlled vocabulary was explored. It does not require training documents, but instead reuses the intellectual work invested into creating the controlled vocabulary. Terms from the Engineering Information thesaurus and classification scheme were matched against text of documents to be classified. Plain string-matching was enhanced in several ways, including term weighting with cut-offs, exclusion of certain terms, and enrichment of the controlled vocabulary with automatically extracted terms. The final results were comparable to those of state-of-the-art machine-learning algorithms, especially for particular classes. Concerning web pages, it was indicated that all the structural information and metadata available in web pages should be used in order to achieve the best automated classification results; however, the exact way of combining them proved not to be very important.In the context of browsing, the biggest difference between three approaches to automated classification (machine learning, information retrieval, library science) is whether they use controlled vocabularies. It has been claimed that well-structured, high-quality classification schemes, such as those used predominantly in library science approaches, could serve as good browsing structures. In the thesis it was shown that Dewey Decimal Classification and Engineering Information classification scheme are suitable for the task. Moreover, a log analysis of a large web-based service using Dewey Decimal Classification demonstrated that browsing is used to a much larger degree than searching.The final conclusion is that an appropriate controlled vocabulary, with a large number of entry vocabulary designating classes, could be utilised in automated classification. If the same controlled vocabulary has an appropriate hierarchical structure, it could at the same time provide a good browsing structure to the automatically classified collection of documents.
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