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

机译:通过Flickr标签分布模拟视觉隐喻术语与语言隐喻术语之间的语义相似性

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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.
机译:这项研究旨在通过基于大数据流的Flickr标签分布方法,对隐喻术语之间的语义相似性进行建模。如文章所述,此分布模型Flickr分布标签空间(FDT)主要捕获概念对之间(即出现在相似标签集中(因此在相似图片中)的标签之间)的关系相似性。隐喻理论的一个长久以来的观点认为,隐喻与意义的概念维度有关,但是尽管不同的模型旨在解释语言如何构建和表示隐喻的概念结构,但我们对其他模态(例如图像)如何实现隐喻的了解仍然很少。构造和表达。主张隐喻的概念性的综合理论不能偏向于对一种特定表达方式的分析和建模,从而忽略了特定于形式的潜在差异。通过FDT进行的本研究发现,视觉和语言隐喻的行为有所不同,因为视觉隐喻中两个对齐的概念之间的相似性似乎比语言隐喻中两个对齐的概念之间的相似性高(反过来, ,与两个随机配对的概念之间的相似性没有实质性差异)。这些发现表明,两个隐喻术语(通过FDT捕获和建模)之间的关系相似性对于视觉隐喻至关重要,但对于语言隐喻却不重要。另外的内容分析(也报告在此)表明,在隐喻的形式方面,相关标签(即构建此分布方法的权变矩阵的上下文)中编码的语义信息的类型有所不同:与相关和与实体有关的特征通常与在视觉隐喻中对齐的概念相关联,自省和分类特征通常与在语言隐喻中对齐的概念相关联。

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