A focus+context technique based on hyperbolic geometry for visualizing large hierarchies
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The effect of information scent on searching information: visualizations of large tree structures
AVI '00 Proceedings of the working conference on Advanced visual interfaces
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
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CHI EA '97 CHI '97 Extended Abstracts on Human Factors in Computing Systems
Degree-of-interest trees: a component of an attention-reactive user interface
Proceedings of the Working Conference on Advanced Visual Interfaces
Melange: space folding for multi-focus interaction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The effects of semantic grouping on visual search
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Comparing Different Properties Involved in Word Similarity Extraction
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Is singular value decomposition useful for word similarity extraction?
Language Resources and Evaluation
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We present an experiment that compares how people perform search tasks in a degree-of-interest browser and in a Windows-Explorer-like browser. Our results show that, whereas users do attend to more information in the DOI browser, they do not complete the task faster than in an Explorer-like browser. However, in both types of browser, users are faster to complete high information scent search tasks than low information scent tasks. We present an ACT-R computational model of the search task in the DOI browser. The model describes how a visual search strategy may combine with semantic aspects of processing, as captured by information scent. We also describe a way of automatically estimating information scent in an ontological hierarchy by querying a large corpus (in our case, Google's corpus).