The nature of statistical learning theory
The nature of statistical learning theory
Making large-scale support vector machine learning practical
Advances in kernel methods
Can Social Tagging Improve Web Image Search?
WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
Easiest-first search: towards comprehension-based web search
Proceedings of the 18th ACM conference on Information and knowledge management
A combined topical/non-topical approach to identifying web sites for children
Proceedings of the fourth ACM international conference on Web search and data mining
Measuring Comprehensibility of Web Pages Based on Link Analysis
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Personalizing web search results by reading level
Proceedings of the 20th ACM international conference on Information and knowledge management
Reading between the tags to predict real-world size-class for visually depicted objects in images
MM '11 Proceedings of the 19th ACM international conference on Multimedia
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Document comprehensibility is one of key factors determining document quality and, in result, user's satisfaction. Relevant web pages are of little utility if they are incomprehensible or impose too much cognitive burden on readers. Traditional measures of text difficulty focus often on syntactic factors of text such as sentence length, word length, syllable count, or they utilize fixed list of common terms. However, document comprehensibility depends on many factors, of which concreteness and the ease of concept visualization are crucial ones. In this paper, we first propose a method for predicting the concreteness of terms using SVM regression. We then extend it to calculating document concreteness level. The experimental results indicate satisfactory accuracy in estimating both term and document concreteness as well as demonstrate positive correlation between the document concreteness and comprehensibility. Our ultimate goal is to enable comprehension-driven search, which will return both relevant and comprehensible results.