Measuring the semantic headedness of English blends with token-based semantic vector space modeling: a corpus-based study.
This article analyzes the semantic headedness of English blends with distributional semantics methods. The semantic head of a blend is the source word that transfers its semantic information to the blend as a whole. For example, a sitcom is a kind of comedy. But is FedEx a kind of express , and is w...
| Publicado en: | Digital Scholarship in the Humanities Vol. 40; pp. 1075 - 1092 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
2025 Supplement
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=182368574&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 182368574 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: 2025 Supplement vid: 40 pid: 622 pub: Oxford University Press / USA artinfo: ui: 182368574 10.1093/llc/fqae050 ppf: 1075 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.7MB tig: atl: Measuring the semantic headedness of English blends with token-based semantic vector space modeling: a corpus-based study. aug: au: Meng, Qingnan Hilpert, Martin affil: School of Foreign Languages, Dalian Maritime University, Dalian, 116026, China Department of English, Université de Neuchâtel, Neuchâtel, 2000, Switzerland su: American English language English language Logistic regression analysis Wireless Internet Corpora sug: subj: American English language English language Logistic regression analysis Wireless Internet Corpora keyword: English blends multinomial logistic regression analysis semantic headedness token-based semantic vector space modeling ab: This article analyzes the semantic headedness of English blends with distributional semantics methods. The semantic head of a blend is the source word that transfers its semantic information to the blend as a whole. For example, a sitcom is a kind of comedy. But is FedEx a kind of express , and is wi-fi a kind of fidelity ? We use corpus data and token-based semantic vector space modeling in order to address these questions. Specifically, we investigate whether Plag's ternary division of endocentric, exocentric, and coordinative compounds based on semantic headedness can also be applied to English blends, and whether the general tendency of semantic right-headedness can be observed for all three subtypes. We analyze a dataset of fifty-five blends and their respective source words, using data from the Corpus of Contemporary American English and the English Web Corpus 2021. We measure the degree of semantic similarity between each blend and its two source words. The results show that for most endocentric blends, the hypothesis of semantic right-headedness holds true. At the same time, exocentric blends and coordinative blends are shown to behave differently. We conclude that Plag's classification offers a useful point of departure for the semantic analysis of blends and that distributional semantics methods can provide new insights into their semantic behavior. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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