Features of Similarity

By Amos Tversky. 1977

Subfields: Cognitive Science & Psychology

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Citations in the corpus

The Unreasonable Effectiveness of Deep Features as a Perceptual Metric (2018, background): “[19, 36, 35], and (3) may not actually constitute a distance metric [55].”
SimLex-999: Evaluating Semantic Models With (Genuine) Similarity Estimation (2014, background): “Association contrasts with similarity , the relation connecting cup and mug (Tversky, 1977).”
UNAL-NLP: Combining Soft Cardinality Features for Semantic Textual Similarity, Relatedness and Entailment (2014, methodology): “The used word-to-word similarity function sim decomposes each word in bags of 3-grams of characters, which are compared using the symmetrical Tversky’s index (Tversky, 1977; Jimenez et al., 2013a).”
Using Information Content to Evaluate Semantic Similarity in a Taxonomy (1995, background): “Rada et al. [1989] suggest that the assessment of similarity in semantic networks can in fact be thought of as involving just taxonomic ( is-a ) links, to the exclusion of other link types; that view will also be taken here, although admittedly it excludes some potentially useful information.”
The (too Many) Problems of Analogical Reasoning with Word Vectors (2017, background): “Logi-cally a is like b is equivalent to b is like a , but humans do not necessarily agree with both statements to the same degree (Tversky, 1977).”
Using similarity criteria to make issue trade-offs in automated negotiations (2002, methodology): “It has, however, been used in work on psychological studies of human behaviour [56], mathematical work on graded extensions of equivalence relations [57, 65], and as a model of approximate reasoning [22, 60].”
Rough sets theory for multicriteria decision analysis (2001, background): “As for the symmetry, one should notice that yRx , which means ‘‘ y is similar to x ’’, is directional; there is a subject y and a referent x , and in general this is not equivalent to the proposition ‘‘ x is similar to y ’’, as maintained by Tversky (1977).”
Reversibility, Aptness, and the Conventionality of Metaphors and Similes (2003, background): “The next step is a comparison process, searching for vehicle properties that can be matched in the topic (Fogelin, 1988; Malgady & Johnson, 1976; Ortony, 1979; Tversky, 1977).”
A Review and Empirical Evaluation of Feature Weighting Methods for a Class of Lazy Learning Algorithms (1997, methodology): “For example, rather than Euclidean or even Minkowskian, distance could be de ned using a set-theoretic de nition (Tversky, 1977; Biberman, 1994) or by a function other than one which sums independent contributions of the features.”
Similarity and analogical reasoning: a synthesis (1989, background): “merely the relative numbers of shared versus nonshared predicates that matter — although that is certainly important, as Tversky (1977)”
The Influence of Macro-Level Motives on Consideration Set Composition in Novel Purchase Situations (2003, background): “In such scenarios, a strategy for simplifying a choice task is to retain alternatives that have overlapping features (Tversky 1972, 1977).”
Category use and category learning. (2003, background): “Under this task, there is much evidence to suggest that people tend to focus on (often a small number of) features that are diagnostic for this goal (Medin et al., 1987; Nosofsky et al., 1994; A. Tversky, 1977).”
Theories of artificial grammar learning. (2007, background): “Moreover, Knowlton and Squire's (1996) chunking measures are an associative learning model of AGL; they have an interpretation in terms of similarity, as feature overlap (Pothos, 2005a; Tversky, 1977), but this similarity is different from specific similarity.”