Show Summary Details

Page of

PRINTED FROM OXFORD HANDBOOKS ONLINE ( © Oxford University Press, 2018. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a title in Oxford Handbooks Online for personal use (for details see Privacy Policy and Legal Notice).

date: 22 February 2020

Abstract and Keywords

This chapter aims to introduce readers not familiar with computational modelling to some approaches and issues in the formal study of learnability, and the relevance of this field to theoretical linguistics and inflectional morphology in particular. After a general overview, the chapter highlights some of the obstacles in learning inflection. Inflection, considered separately from other components of language, is relatively restricted in its expressive power, which should make it easier to learn than syntax. However, inflectional systems are full of irregularities and mismatches between different levels of structure, and such irregularities make learning difficult. Overall, it is concluded that linguistically interesting proposals for machine learning of inflection should provide explanations for the nature and extent of irregularities and for the specific patterns of language acquisition and language change.

Keywords: learnability, machine learning, acquisition of inflection, learning biases, inflectional mismatches

Access to the complete content on Oxford Handbooks Online requires a subscription or purchase. Public users are able to search the site and view the abstracts and keywords for each book and chapter without a subscription.

Please subscribe or login to access full text content.

If you have purchased a print title that contains an access token, please see the token for information about how to register your code.

For questions on access or troubleshooting, please check our FAQs, and if you can''t find the answer there, please contact us.