Modeling Semantic Similarity between Metaphor Terms of Visual vs. Linguistic Metaphors through Flickr Tag Distributions

Frontiers in Communication

Bolognesi, Marianna¹ 

[1] Metaphor Lab Amsterdam, Department of Argumentation Theory and Rhetoric, University of Amsterdam, Netherlands

关键词: metaphor;    semantic similarity;    distributional semantics;    visual metaphors;    Linguistic metaphors;    big data;    Flickr;   

DOI  :  10.3389/fcomm.2016.00009

学科分类：计算机网络和通讯

来源: Frontiers

【 摘 要 】

This study aims at modeling the semantic similarity between metaphor terms by means of a distributional method based on a Big Data stream: Flickr tags. As explained in the article, this distributional model, Flickr Distributional Tagspace (FDT), captures primarily relational similarity between concept pairs, that is, between tags that appear in similar tagsets (and therefore in similar pictures). A long established view in metaphor theory claims that metaphors pertain to the conceptual dimension of meaning, but while different models aim at explaining how language constructs and represents metaphorical conceptual structures, we still know very little about how other modalities (for example images) achieve metaphor construction and expression. A comprehensive theory, that argues in favor of the conceptual nature of metaphor, cannot afford to be biased toward the analysis and modeling of one specific modality of expression, thus neglecting potential modality-specific differences. The present study, conducted through FDT, found that visual and linguistic metaphors behave differently, in that the similarity between two aligned concepts in a visual metaphor appears to be significantly higher than the similarity between two concepts aligned in a linguistic metaphor (which, in turn, does not differ substantially from the similarity between two randomly paired concepts). These findings suggest that the relational similarity between two metaphor terms (captured and modeled through FDT) is crucial for visual metaphors but not for linguistic metaphors. An additional content analysis, also reported here, shows that the type of semantic information encoded in the related tags (i.e. the contexts on which the contingency matrices of this distributional method are built) differs, in relation to the modality of the metaphor: while situation-related and entity-related features are typically associated with concepts aligned in visual metaphors, introspections and taxonomic features are typically associated with concepts aligned in linguistic metaphors.

【 授权许可】

CC BY