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).

In this paper we describe a comparison among three systems that estimate semantic similarity between words: Latent Semantic Analysis [6], Pointwise Mutual Information [17], and Generalized Latent Semantic Analysis [8]. We compare all these techniques on a unique corpus (TASA) and, for PMI and GLSA, we also report performance on a different web-based corpus. The evaluation is carried out through two kinds of tests: (1) synonymy tests, and (2) comparison with human word similarity judgments.

We investigate how people process negation in semantically distorted and metaphoric sentences. We present three experiments in which participants judged the truth of a_rmative and negative sentences that were either literal or contained semantic illusions (Erickson & Mattson, 1981) or metaphors. In all experiments, negation increases processing times; although for semantic illusions, negation preserves the ordering of judgment times for literal and nonliteral sentences, for metaphors this ordering is reversed, with nonliteral negatives taking less time than literal negatives. This result presents evidence against the traditional Clark and Chase (1972) model of negation. We propose the negation-as distortion model and discuss how this model can explain the data.

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