What makes a metaphor literary? Answers from two computational studies.
In this article we investigate structural differences between “literary” metaphors created by renowned poets and “nonliterary” ones imagined by non-professional authors from Katz et al.’s 1988 corpus. We provide data from quantitative narrative analyses (QNA) of the altogether 464 metaphors on over...
| Publicado en: | Metaphor & Symbol Vol. 33; no. 2; pp. 85 - 101 |
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| Autores principales: | , |
| Formato: | Artículo |
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Taylor & Francis Ltd
Apr-Jun2018
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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=ssf&AN=129301174&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 129301174 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10926488 7MO jtl: Metaphor & Symbol issn: 10926488 maglogo: N pubinfo: dt: Apr-Jun2018 vid: 33 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 129301174 10.1080/10926488.2018.1434943 ppf: 85 ppct: 16 formats: fmt: – @attributes: type: T db: hlh ui: 129301174 – @attributes: type: P db: hlh ui: 129301174 tig: atl: What makes a metaphor literary? Answers from two computational studies. aug: au: Jacobs, Arthur M. Kinder, Annette affil: Freie Universität Berlin su: Quantitative research Quantitative chemical analysis Absorbance matching Semantics (Philosophy) Machine learning sug: subj: Quantitative research Quantitative chemical analysis Absorbance matching Semantics (Philosophy) Machine learning ab: In this article we investigate structural differences between “literary” metaphors created by renowned poets and “nonliterary” ones imagined by non-professional authors from Katz et al.’s 1988 corpus. We provide data from quantitative narrative analyses (QNA) of the altogether 464 metaphors on over 70 variables, including surface features like metaphor length, phonological features like sonority score, or syntactic-semantic features like sentence similarity. In a first computational study using machine learning tools (i.e., a classifier of the decision tree family) we show that Katz et al.’s literary metaphors can be successfully discriminated from their nonliterary ones on the basis of <italic>response measures</italic> (10 ratings), in particular the ratings for familiarity, ease of interpretation, semantic relatedness, and comprehensibility. A second computational study then shows that the classifier can reliably detect and predict between-group differences on the basis of five QNA features generalizing from a training to a test corpus. Our results shed light on surface and semantic features that co-determine the reception of metaphors and raise important questions about their literariness, aptness or poetic potential. They tentatively suggest a set of 11 features that could influence the “literariness” of metaphors, including their sonority score, length and surprisal value. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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