Towards a model for the prediction of Chinese novel verbs

Paul You Jun Chang, Kathleen Virginia Ahrens

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

Abstract

As previous word adoption models, though proposing potential factors that influence the survival of neologisms, receive little empirical examination, this corpus-based study compares the performance of two such models by providing clear operational criteria for each factor in the models and, consequently, proposes a hybrid model that improves the previous results. We focus on seventy-seven Chinese novel verbs that appeared about ten years ago, defining their survival/failure in the real world, and examine the accurate prediction ratio of the two models. Both models display an overall accuracy of about 60 percent. However, as certain factors, e.g., unobtrusiveness, appear to be invalid predictors for the Chinese data, we attempt to improve the results by deleting inappropriate factors and by adjusting the weightings. As the overall accuracy was improved to about 70 percent, we suggest that this study would shed light on the potential factors that influence the survival of Chinese novel verbs.
Original languageEnglish
Title of host publicationProceedings of the 22nd Pacific Asia Conference on Language, Information and Computation, PACLIC 22
Pages131-140
Number of pages10
Publication statusPublished - 1 Dec 2008
Externally publishedYes
Event22nd Pacific Asia Conference on Language, Information and Computation, PACLIC 22 - Cebu, Philippines
Duration: 20 Nov 200822 Nov 2008

Conference

Conference22nd Pacific Asia Conference on Language, Information and Computation, PACLIC 22
CountryPhilippines
CityCebu
Period20/11/0822/11/08

Keywords

  • Neologism
  • Unobtrusiveness
  • Word adoption model

ASJC Scopus subject areas

  • Language and Linguistics
  • Computer Science (miscellaneous)
  • Information Systems

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