Exploring the feeling-emotions continuum across cultures: Jealousy in English and Spanish

Díaz-Vera, Javier E.; Caballero, Rosario

doi:10.1515/ip-2013-0012

Cited by 11 publications

(9 citation statements)

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“…However, it should be noted that the generalization to coarse semantic features is likely to focus on shallow properties of metaphorical language and to bypass conceptual information. Corpus-linguistic research (Charteris- Black and Ennis 2001;Kovecses 2005;Diaz-Vera and Caballero 2013) suggests that there is considerable variation in metaphorical language across cultures, which makes training only on one language and translating the model problematic for modeling conceptual structure behind metaphor.…”

Section: Metaphor Identification As Supervised Classificationmentioning

confidence: 99%

Multilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised Learning

Shutova

Sun²,

Gutiérrez

et al. 2017

Computational Linguistics

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Highly frequent in language and communication, metaphor represents a significant challenge for Natural Language Processing (NLP) applications. Computational work on metaphor has traditionally evolved around the use of hand-coded knowledge, making the systems hard to scale. Recent years have witnessed a rise in statistical approaches to metaphor processing. However, these approaches often require extensive human annotation effort and are predominantly evaluated within a limited domain. In contrast, we experiment with weakly supervised and unsupervised techniques—with little or no annotation—to generalize higher-level mechanisms of metaphor from distributional properties of concepts. We investigate different levels and types of supervision (learning from linguistic examples vs. learning from a given set of metaphorical mappings vs. learning without annotation) in flat and hierarchical, unconstrained and constrained clustering settings. Our aim is to identify the optimal type of supervision for a learning algorithm that discovers patterns of metaphorical association from text. In order to investigate the scalability and adaptability of our models, we applied them to data in three languages from different language groups—English, Spanish, and Russian—achieving state-of-the-art results with little supervision. Finally, we demonstrate that statistical methods can facilitate and scale up cross-linguistic research on metaphor.

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Section: Metaphor Identification As Supervised Classificationmentioning

confidence: 99%

Multilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised Learning

Shutova

Sun²,

Gutiérrez

et al. 2017

Computational Linguistics

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“…In addition, these approaches often focused on a small predefined set of source and target domains. Another vein of corpus-based research concerned crosslinguistic differences in the use of metaphor, also in a specific domain-for example, financial discourse (Charteris-Black and Ennis 2001), metaphors describing FEELINGS (Stefanowitsch 2004;Diaz-Vera and Caballero 2013), or metaphorical expressions referring to body parts (Deignan and Potter 2004). Three recent studies are notable in that they moved away from investigating particular domains to a more general study of how metaphor behaves in unrestricted continuous text.…”

Section: Corporamentioning

confidence: 99%

Design and Evaluation of Metaphor Processing Systems

Shutova

2015

Computational Linguistics

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System design and evaluation methodologies receive significant attention in natural language processing (NLP), with the systems typically being evaluated on a common task and against shared data sets. This enables direct system comparison and facilitates progress in the field. However, computational work on metaphor is considerably more fragmented than similar research efforts in other areas of NLP and semantics. Recent years have seen a growing interest in computational modeling of metaphor, with many new statistical techniques opening routes for improving system accuracy and robustness. However, the lack of a common task definition, shared data set, and evaluation strategy makes the methods hard to compare, and thus hampers our progress as a community in this area. The goal of this article is to review the system features and evaluation strategies that have been proposed for the metaphor processing task, and to analyze their benefits and downsides, with the aim of identifying the desired properties of metaphor processing systems and a set of requirements for their evaluation.

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“…These studies have revealed that, in contemporary language use, human emotions are often understood or conceptualized in metaphorical terms (e.g. Díaz-Vera & Caballero, 2013;Kahumburu, 2016;Ogarkova, 2007;Rajeg, 2019). Their findings highlight the importance of metaphor in human thought and language, although, in historical language use, expressions of emotions have been found to be largely literal in some research (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Conceptualizations of human emotions have been a research topic of great interest in linguistics, for there have been many studies on various emotion concepts, such as ANGER (Glynn, 2014;Kahumburu, 2016;Yu, 1995), FEAR (Caballero & Díaz-Vera, 2021;Oster, 2010Oster, , 2012, HAPPINESS (Nguyen, 2016;Rajeg, 2019;Stefanowitsch, 2004), JEALOUSY (Díaz-Vera & Caballero, 2013;Ogarkova, 2007), LOVE (Gawda, 2019;Glynn, 2002), PRIDE (Soares da Silva, 2020), SADNESS (Verdaguer & Castaño, 2018), and SHAME (Krawczak, 2014a(Krawczak, , 2014b(Krawczak, , 2018. These studies have revealed that, in contemporary language use, human emotions are often understood or conceptualized in metaphorical terms (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The study also indicates that JEALOUSY is perceived as a significantly more painful and unwelcome emotion than ENVY. Díaz-Vera and Caballero (2013) presented a corpus study of the ways that JEALOUSY is metaphorically construed in American English and Spanish, focusing on the role of sensorial perception in expressing this emotion across genres. A major finding of their study is that while touch figures prominently in both languages, American English speakers make much more frequent use of a wide variety of sensory-related metaphors (including vision, hearing, smelling, and tasting) than in Spanish, foregrounding the physical component of this emotion.…”

Section: Introductionmentioning

confidence: 99%

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Exploring metaphorical conceptualizations of ENVY in English and Chinese: A multifactorial corpus analysis

Wu,

Liu

2023

Lang. cogn.

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Adopting a multifactorial analysis, this corpus-based comparative study examines the metaphorical conceptualizations of ENVY in American English and Chinese. All the metaphorical occurrences of ENVY in the two languages were collected from the corpora and then submitted to a detailed semantic and usage analysis. The qualitative and quantitative analyses show both striking similarities and differences in the metaphorical structuring of ENVY across the two languages. Both languages resort to largely the same common conceptual metaphors but use divergent elements from these metaphors’ source domains in their conceptualizations of ENVY. Moreover, a correspondence analysis of the most frequently used envy metaphors in relation to four important factors reveals some noticeable cross-language differences in their behavioural profiles. Based on the corpus findings, this study also explores the motivations underlying the similarities and differences in the metaphorical structuring of ENVY. The results indicate that the similarities are grounded mainly in common bodily experiences, whereas the differences are motivated by either differential cultural experiences or differential cognitive preferences.

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Exploring the feeling-emotions continuum across cultures: Jealousy in English and Spanish

Cited by 11 publications

References 0 publications

Multilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised Learning

Multilingual Metaphor Processing: Experiments with Semi-Supervised and Unsupervised Learning

Design and Evaluation of Metaphor Processing Systems

Exploring metaphorical conceptualizations of ENVY in English and Chinese: A multifactorial corpus analysis

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