Topic
Contrast set
About: Contrast set is a research topic. Over the lifetime, 30 publications have been published within this topic receiving 805 citations.
Papers
TL;DR: Two visual world experiments demonstrate that uttering a referring expression with a scalar adjective makes all members of the relevant contrast set more salient in the discourse model, facilitating subsequent reference to other members of that contrast set.
42 citations
TL;DR: This work explores the nature of the evidence necessary for the modulation of pragmatic inferences in language comprehension, focusing on the complementary roles of top-down information (knowledge about the particular speaker's pragmatic competence) and bottom-up cues and distributional information about the use of scalar adjectives in the environment.
31 citations
TL;DR: It is shown that this task can be effectively solved by transforming it to a more common and well-understood subgroup discovery task, and the explanatory potential of discovered contrast sets can be improved by offering additional contrast set descriptors, called the supporting factors.
29 citations
TL;DR: An algorithm to discover negative contrasts sets across groups, and establish some properties to accelerate the execution of the algorithm is proposed and then applied on an insurance data set to find meaningful negative contrast sets that can assist in designing insurance programs for various types of customers.
Abstract: Negative contrast sets show that the relationships between the existence of some characteristics and the nonexistence of some other characteristics for various groups are significantly different. These sets can provide additional information for making decisions. Mining positive contrast sets like 'X and Y' is relatively straightforward and computationally efficient with respect to mining negative contrast sets like 'not X and Y'. In this paper, we propose an algorithm to discover negative contrasts sets across groups, and establish some properties to accelerate the execution of the algorithm. It is then applied on an insurance data set to find meaningful negative contrast sets that can assist in designing insurance programs for various types of customers.
24 citations
29 Jun 2015
TL;DR: A novel algorithm, SciCSM, is presented for efficient contrast set mining over array-based datasets, defining how "interesting" contrast sets can be characterized for numeric and array data -- handling the fact that subsets can involve both value-based and/or dimension-based attributes.
Abstract: Contrast set mining is a broadly applicable exploratory technique, which identifies interesting differences across contrast groups. The existing algorithms primarily target relational datasets with categorical attributes. There is clearly a need to apply this method to discover interesting patterns across scientific datasets, which feature arrays with numeric values. In this paper, we present a novel algorithm, SciCSM, for efficient contrast set mining over array-based datasets. We define how "interesting" contrast sets can be characterized for numeric and array data -- handling the fact that subsets can involve both value-based and/or dimension-based attributes. We extensively use bitmap indices to reduce computational complexity and enable processing of larger-scale data. We demonstrate both high efficiency and effectiveness of our algorithm by using multiple real-life datasets.
20 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 1 |
| 2020 | 1 |
| 2019 | 2 |
| 2018 | 1 |
| 2016 | 3 |
| 2015 | 5 |