The Chinese Classifier System as a Lexical-semantic System

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

Abstract

This chapter shows that treating the Chinese classifier system as a lexicalized semantic system based on shared ontology predicts both the agreement patterns that motivate the structure-based accounts, and the semantic selection patterns that motivate the cognition-based accounts. In the chapter, different perspectives toward classifiers are introduced including a cognition-based account (predicting a strong correlation with perception that is also robust and without exceptions but allows some fuzzy, overlapping classifications) and a structure-based account (predicting a strongly binary classification and a robust grammaticality judgement). Controversial issues regarding Chinese classifiers, such as the distinction between classifiers and measure words, the agreement between a classifier and its head noun, and the nature of 的 DE insertion, are explicated to show the pros and cons of various approaches. The authors demonstrate that Chinese classifiers are coherently organized in a ontology-driven lexical-semantic system. Major unresolved issues in the Mandarin classifiers system are closely examined at the end of the chapter.
Original languageEnglish
Title of host publicationThe Cambridge Handbook of Chinese Linguistics
EditorsChu-Ren Huang, Yen-Hwei Lin, I-Hsuan Chen, Yu-Yin Hsu
PublisherCambridge University Press
Chapter25
Pages550-577
ISBN (Electronic)9781108329019
ISBN (Print)9781108420075
DOIs
Publication statusPublished - 4 Aug 2022

Publication series

NameCambridge Handbooks in Language and Linguistics
PublisherCambridge University Press

Keywords

  • Chinese classifiers
  • sortal classifiers
  • ontology-driven
  • lexical-semantic system
  • cognition-based account
  • structure-based account

Fingerprint

Dive into the research topics of 'The Chinese Classifier System as a Lexical-semantic System'. Together they form a unique fingerprint.

Cite this