When embodiment meets generative lexicon: The human body part metaphors in Sinica corpus

Ren Feng Duann, Chu-ren Huang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)

Abstract

This research aims to integrate embodiment with generative lexicon. By analyzing the metaphorically used human body part terms in Sinica Corpus, the first balanced modern Chi-nese corpus, we reveal how these two theories complement each other. Embodiment strengthens generative lexicon by spelling out the cognitive reasons which underlies the pro-duction of meaning, and generative lexicon, specifically the qualia structure, complements embodiment by accounting for the reason un-derlying the selection of a particular body part for metaphorization. Discussing how the four body part terms-xie "blood", rou "flesh", gu "bone", mai "meridian"-behave metaphorically, this research argues that the visibility and the telic role of the qua-lia structure are the major reasons motivating the choice of a body part to represent a com-paratively abstract notion. The finding ac-counts for what constrains the selection of body parts for metaphorical uses. It also facili-tates the prediction of the behavior of the four body part terms in these uses, which can func-tion as the starting point to examine whether the two factors-visibility and telicity-also motivate the metaphorization of the rest hu-man body parts.
Original languageEnglish
Title of host publication29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015
PublisherShanghai Jiao Tong University
Pages396-403
Number of pages8
Publication statusPublished - 1 Jan 2015
Event29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015 - Shanghai, China
Duration: 30 Oct 20151 Nov 2015

Conference

Conference29th Pacific Asia Conference on Language, Information and Computation, PACLIC 2015
CountryChina
CityShanghai
Period30/10/151/11/15

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Linguistics and Language

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