A new comprehensive set of concept feature norms

2015 ◽  
Vol 44 ◽  
pp. 111-120
Author(s):  
Eleonora Catricalà ◽  
Valeria Ginex ◽  
Chiara Dominici ◽  
Stefano F. Cappa
Keyword(s):  
Feature Norms

Defending International Norms: The Role of Obligation, Material Interest, and Perception in Decision Making

2001 ◽  
Vol 55 (3) ◽  
pp. 621-654 ◽  
Author(s):  
Richard K. Herrmann ◽  
Vaughn P. Shannon
Keyword(s):  
Decision Making ◽  
Foreign Policy ◽  
International Relations ◽  
Normative Reasoning ◽  
Cross Border ◽  
Material Interest ◽  
Individual Perceptions ◽  
Material Interests ◽  
Feature Norms

States defend norms in some cases but not in others. Understanding this variation sheds light on both U.S. foreign policy and the role of normative reasoning. We report the results of four experiments embedded in a survey of U.S. elites. The experiments identified the effects of felt normative obligation (that is, the logic of what is appropriate) and concern for U.S. economic and security interests (that is, the logic of utilitarian consequence) as well as the role played by individual perceptions. We find that perceptions of another actor's motivation, of conflicts as civil or cross-border wars, and of the democratic nature of victims affect decisions to defend a prescriptive norm. This finding means that theories of international relations that feature norms as structural concepts need to consider actor-level cognition when examining the operation of norms. Moreover, we find that when U.S. economic and security interests are at stake there is a much greater inclination to defend norms than when simply normative obligation is present. Most U.S. elites appear to treat the presence or absence of U.S. material interests as a legitimate criterion for deciding whether or not to defend an international prescriptive norm.

scholarly journals BLIND: a set of semantic feature norms from the congenitally blind

2013 ◽  
Vol 45 (4) ◽  
pp. 1218-1233 ◽  
Author(s):  
Alessandro Lenci ◽  
Marco Baroni ◽  
Giulia Cazzolli ◽  
Giovanna Marotta
Keyword(s):  
Semantic Feature ◽  
Congenitally Blind ◽  
Feature Norms

Combining feature norms and text data with topic models

2010 ◽  
Vol 133 (3) ◽  
pp. 234-243 ◽  
Author(s):  
Mark Steyvers
Keyword(s):  
Topic Models ◽  
Text Data ◽  
Feature Norms

scholarly journals Using semantic feature norms to investigate how the visual and verbal modes afford metaphor construction and expression

2016 ◽  
Vol 9 (3) ◽  
pp. 525-552 ◽  
Author(s):  
MARIANNA BOLOGNESI
Keyword(s):  
Semantic Similarity ◽  
Semantic Features ◽  
Linguistic Processing ◽  
Conceptual Content ◽  
Generation Task ◽  
Metaphor Comprehension ◽  
Conceptual Processing ◽  
Figures Of Speech ◽  
Visual Metaphors ◽  
Feature Norms

abstractIn this study, two modalities of expression (verbal and visual) are compared and contrasted, in relation to their ability and their limitations to construct and express metaphors. A representative set of visual metaphors and a representative set of linguistic metaphors are here compared, and the semantic similarity between metaphor terms is modeled within the two sets. Such similarity is operationalized in terms of semantic features produced by informants in a property generation task (e.g., McRae et al., 2005). Semantic features provide insights into conceptual content, and play a role in deep conceptual processing, as opposed to shallow linguistic processing. Thus, semantic features appear to be useful for modeling metaphor comprehension, assuming that metaphors are matters of thought rather than simple figures of speech (Lakoff & Johnson, 1980). The question tackled in this paper is whether semantic features can account for the similarity between metaphor terms of both visual and verbal metaphors. For this purpose, a database of semantic features was collected and then used to analyze fifty visual metaphors and fifty verbal metaphors. It was found that the number of semantic features shared between metaphor terms is predicted by the modality of expression of the metaphor: the terms compared in visual metaphors share semantic features, while the terms compared in verbal metaphors do not. This suggests that the two modalities of expression afford different ways to construct and express metaphors.

A cross-linguistic comparison of Spanish and English semantic norms: Looking at core features

2020 ◽  
Vol 41 (2) ◽  
pp. 285-297
Author(s):  
Jorge Vivas ◽  
Boris Kogan ◽  
Sofía Romanelli ◽  
Francisco Lizarralde ◽  
Luis Corda
Keyword(s):  
Dimensional Space ◽  
Specific Pattern ◽  
Cultural Foundations ◽  
Core Concepts ◽  
Semantic Norms ◽  
Sociocultural Diversity ◽  
Geometric Technique ◽  
Feature Norms ◽  
Concrete Concepts ◽  
Core Features

AbstractIt has been suggested that human communities that share their basic cultural foundations evince no remarkable differences concerning the characterization of core concepts. However, the small but existing differences among them reflect their sociocultural diversity. This study compares 219 concrete concepts common to both Spanish and English semantic feature norms in order to assess whether core features of concepts follow a universal or cultural language-specific pattern. Concepts were compared through a geometric technique of vector comparison in the Euclidean n-dimensional space alongside the calculation of the network’s degree of centrality. The role of cognate status was also explored by repeating the former analysis separating cognate from noncognate words. Taken together, our data show that languages are structurally similar independent of the cognate status of words, further suggesting that there are some sort of core features common to both languages.

scholarly journals Learning Neural Audio Embeddings for Grounding Semantics in Auditory Perception

2017 ◽  
Vol 60 ◽  
pp. 1003-1030 ◽  
Author(s):  
Douwe Kiela ◽  
Stephen Clark
Keyword(s):  
Neural Networks ◽  
Auditory Perception ◽  
Deep Neural Networks ◽  
Image Data ◽  
Auditory Information ◽  
Semantic Representations ◽  
Modal Semantics ◽  
Feature Norms ◽  
Auditory Data

Multi-modal semantics, which aims to ground semantic representations in perception, has relied on feature norms or raw image data for perceptual input. In this paper we examine grounding semantic representations in raw auditory data, using standard evaluations for multi-modal semantics. After having shown the quality of such auditorily grounded representations, we show how they can be applied to tasks where auditory perception is relevant, including two unsupervised categorization experiments, and provide further analysis. We find that features transfered from deep neural networks outperform bag of audio words approaches. To our knowledge, this is the first work to construct multi-modal models from a combination of textual information and auditory information extracted from deep neural networks, and the first work to evaluate the performance of tri-modal (textual, visual and auditory) semantic models.

Sign in / Sign up

Export Citation Format

Share Document