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Title:
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MEASURING EFFECTIVENESS OF TEXT-DECORATED HTML TAGS IN WEB DOCUMENT CLUSTERING |
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Author(s):
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Mark P. Sinka , David W. Corne |
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ISBN:
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972-99353-0-0 |
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Editors:
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Pedro Isaías and Nitya Karmakar |
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Year:
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2004 |
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Edition:
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1 |
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Keywords:
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Web Document Analysis, HTML Text Decoration, Web Document Categorisation. |
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Type:
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Full Paper |
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First Page:
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707 |
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Last Page:
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714 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Web document analysis, and its associated research, underpins much of what is referred to as web intelligence and the envisaged semantic web. A key issue in this field is how to encode a web document from the raft of potential document features without losing salient information. Current research almost always uses word-based feature vectors such as term frequency of specific words (TF) and/or variants such as normalised term frequency and TF*IDF. We explore the question of whether existing word-based term vectors can be usefully augmented by using text-decorated words delimited by the HTML tag. We measure the effectiveness of a feature vector by encoding documents from a benchmark set in terms of this feature vector, and then measuring the accuracy of an unsupervised clustering task using this encoding. A thorough investigation is performed, using a variety of parameter values, to explore whether any increase is accuracy is achieved over vectors constructed just from the plain document text. Tests on the BankSearch dataset showed 9 different parameter combinations (using the text-decorating tag words) that had an improved accuracy over the vectors obtained via the plain document text. |
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