Introduction: A bird’s-eye view of lexical blending

Renner, Vincent; Maniez, François; Arnaud, Pierre

doi:10.1515/9783110289572.1

Cited by 26 publications

(11 citation statements)

References 2 publications

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“…The problematic issue of defining what is a lexical blend (and not) further complicates the quantitative analyses [Bauer 2012]. One perspective that may contribute to developing the notion of category delineation is to take a prototype theory perspective [López Rúa 2004;Brdar-Szabó & Brdar 2008;Bauer 2012;Renner et al 2012]. It follows from such a viewpoint that holistic analyses of the objects under investigation are performed, and that attributes, or characteristics, are studied in relation to their distribution among the data.…”

Section: Discussionmentioning

confidence: 99%

“…This is questionable for at least two reasons. First, there is a growing understanding of lexical blends in terms of prototypically organized categories [López Rúa 2004;Brdar-Szabó & Brdar 2008;Bauer 2012;Renner et al 2012;Renner 2015], which implies that identification and analyses relying on formal semantics listing of necessary and sufficient conditions are inherently misleading. It has even been argued that such approaches are (at least partly) responsible for the 20 th century failures to account successfully for the complexity of lexical blending [Fandrych 2008].…”

Section: Discussionmentioning

confidence: 99%

“…Many attempts have been made to define lexical blends, but as matters stand only preliminary attempts are at hand [Bauer 2012;Renner et al 2012;Beliaeva 2014], andKemmer [2003: 71] argues that "[l]exical blends are so varied in form that no neat taxonomy can do justice to the full range of the phenomenon." Nonetheless, a very brief but relatively uncontroversial description of a lexical blend can be summed up as follows:…”

Section: Definitionsmentioning

confidence: 99%

“…Further examples of issues that remain largely unresolved are the presumed semantic relatedness between the lexemes that produce the constituent segments, traditionally called the source words (SW), of a blend [Algeo 1977;Brdar-Szabó & Brdar 2008] and the prototypicality of certain formation patterns such as distribution of truncation strategies and source word overlap [Renner et al 2012;Arndt-Lappe & Plag 2013].…”

Section: Introduction and Aims Of The Studymentioning

confidence: 99%

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Gold Punning: studying multistable meaning structures using a systematically collected set of lexical blends

Kjellander¹

2018

lexis

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Definitionsmentioning

confidence: 99%

Section: Introduction and Aims Of The Studymentioning

confidence: 99%

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Gold Punning: studying multistable meaning structures using a systematically collected set of lexical blends

Kjellander¹

2018

lexis

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“…For example, brunch (breakfast + lunch), fantabulous (fantastic + fabulous), entertoyment (entertainment + toy) etc. This linguistic form of word reduction has been studied widely [8,10,12,13,14,24,36,44,50].…”

Section: Lexical Blendingmentioning

confidence: 99%

#Bieber + #Blast = #BieberBlast

Maity

Saraf

Mukherjee

2016

Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work &Amp; Social Computing

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Compounding of natural language units is a very common phenomena. In this paper, we show, for the first time, that Twitter hashtags which, could be considered as correlates of such linguistic units, undergo compounding. We identify reasons for this compounding and propose a prediction model that can identify with 77.07% accuracy if a pair of hashtags compounding in the near future (i.e., 2 months after compounding) shall become popular. At longer times T = 6, 10 months the accuracies are 77.52% and 79.13% respectively. This technique has strong implications to trending hashtag recommendation since newly formed hashtag compounds can be recommended early, even before the compounding has taken place. Further, humans can predict compounds with an overall accuracy of only 48.7% (treated as baseline). Notably, while humans can discriminate the relatively easier cases, the automatic framework is successful in classifying the relatively harder cases.

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Analogy and contrast at the morphology-syntax interface

Sokolova

2022

Human Cognitive Processing

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This chapter brings together the issues of analogy and contrast through a perspective of linguistic borrowing. We analyze the integration of new [N[N]] compounds, borrowed from English into Russian (e.g. top-igrok ‘top player’). By discussing two corpus studies and two small experiments, we show that Russian new [N[N]] compounds reflect both “levelling” and “extension”. There are at least three factors that can block “leveling”: statistical preemption, which is overridden by pragmatic factors; multiplicity of competing alternatives, when several suffixes could be added to the stem to form a relational adjective; and semantics that leads to a split between the two forms. Compound modifiers seem to represent terms and official titles, whereas adjective phrases become qualitative, where possible, or pragmatic.

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Introduction: A bird’s-eye view of lexical blending

Cited by 26 publications

References 2 publications

Gold Punning: studying multistable meaning structures using a systematically collected set of lexical blends

Gold Punning: studying multistable meaning structures using a systematically collected set of lexical blends

#Bieber + #Blast = #BieberBlast

Analogy and contrast at the morphology-syntax interface

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